Platform and applications for wireless location and other complex services

ABSTRACT

A network services architecture is disclosed that allows complex services, such as wireless location services, to be more easily integrated into a telecommunications network. In particular, location based services utilizing a wireless location gateway can be more easily integrated into the network. Additionally, disclosed are techniques for facilitating the handling of requests for emergency services wherein the location of mobile station is required.

RELATED APPLICATIONS

The present application is a continuation in part of U.S. patentapplication Ser. No. 10/297,449 filed Dec. 6, 2002, and is also acontinuation-in-part of U.S. application Ser. No. 09/194,367 filed Nov.24, 1998, and is also a continuation in part of U.S. patent applicationSer. No. 10/337,807 filed Jan. 6, 2003, which claims the benefit of U.S.Provisional Patent Application Ser. No. 60/349,100 filed Jan. 4, 2002.The entire disclosure of the above-identified patent applications areincorporated by reference herein.

FIELD OF THE INVENTION

The present invention is directed generally to a system and method forproviding complex network services requiring interactions betweenvarious network accessible applications and/or services, and inparticular where such complex services utilize or require the locationof a wireless mobile station, and more particularly, to the utilizationof a plurality wireless location service providers via, e.g., a wirelesslocation gateway. Additionally, the present invention is directed to aplatform for enabling such complex services, and to identifying suchnovel services that may be provided by such a platform. Thus, thepresent invention is directed to complex network services such aslocation based services for locating people or objects, and inparticular, to a system and method for locating wireless mobilestations. The present invention is further directed to combininglocation results from a plurality of mobile station location estimatorssuch as is provided by a wireless location gateway.

BACKGROUND

There is great interest in providing existing infrastructures forwireless communication systems with the capability for locating peopleand/or objects in a cost effective manner. Such a capability would beinvaluable in a variety of situations, especially in emergency, crimesituations and mobile commerce. There are numerous competing wirelesslocation technologies that purport to effectively locate wireless mobilestations (as used herein this term includes, e.g., mobile phones, shortmessage devices (SMS), electronic container tracking tags,micro-transceivers for personal location and/or emergency, and mobiletransmitters such as can be used on battlefield or militaryreconnaissance, surveillance or tracking; additionally, in a moregeneral context, this term includes vehicles, and other mobile unitssuch as railroad cars, watercraft, and aircraft containing a device thatcan be located wirelessly). These technologies can be generallyclassified as:

-   -   (a) handset centric wherein a portion of the location processing        is performed at the mobile stations, and in particular, each        such mobile station (MS) includes specialized electronics        specifically for performing location. In most cases, such        specialized electronics are for detecting and receiving        satellite (or more generally, non-terrestrial transmitters        and/or transceivers) signals that can then be used in        determining a location of the MS;    -   (b) network centric wherein the wireless communication        network(s) with which the MS is in contact handle substantially        all location specific processing. As one skilled in the art will        understand, there are various wireless location technologies        that are available such as location technologies based on time        difference of arrival (TDOA), time of arrival (TOA), timing        advance (TA) techniques, angle of arrival (AOA), multipath        pattern matching techniques; and    -   (c) hybrid systems wherein there are specialized location        electronics at the handset (“handset” being used herein as an        equivalent to mobile station unless stated otherwise), but a        non-trivial amount of the location processing is performed at a        network site rather at the MS. An example of such a hybrid        system is what is known as network assisted GPS systems, wherein        GPS signals are obtained at the MS (with the assistance network        received information) and GPS timing information is transmitted        from the MS to the network for performing MS location        computations.

The wide variety of wireless location techniques can provide, underappropriate circumstances, the following advantages:

-   -   (a) if the techniques are used in combination, a more reliable        and accurate wireless location capability can be provided. In        particular, when an embodiment of one wireless location        technique is known to be less than satisfactory in a particular        geographic area, an alternative embodiment (or alternative        technique) can be used to obtain an MS's location(s).        Additionally, two different embodiments and/or techniques can be        applied substantially simultaneously for locating an MS. In this        latter case, a location resolver is likely needed to determine a        “most likely” resulting MS location estimate. Note, that        wireless location systems for combining wireless location        techniques is described in the following international and U.S.        patent applications which are each incorporated fully by        reference herein:        -   i. U.S. Provisional Patent Application No. 60/025,855 filed            Sep. 9, 1996;        -   ii. U.S. Provisional Patent Application No. 60/044,821,            filed Apr. 25, 1997;        -   iii. U.S. Provisional Application No. 60/056,590, filed Aug.            20, 1997;        -   iv. International Patent Application No. PCT/US97/15933            filed Sep. 8, 1997 entitled “LOCATION OF A MOBILE STATION            USING A PLURALITY OF COMMERCIAL WIRELESS INFRASTRUCTURES” by            LeBlanc, Dupray, and Karr;        -   v. International Patent Application No. PCT/US97/15892 filed            Sep. 8, 1997; entitled “LOCATION OF A MOBILE STATION” by            Dupray, and Karr        -   vi. U.S. patent application Ser. No. 09/194,367 filed Nov.            24, 1999 entitled “Location Of A Mobile Station” by Dupray,            and Karr;        -   vii. U.S. patent application Ser. No. 09/176,587 filed Oct.            21, 1998 entitled “Wireless Location System For Calibrating            Multiple Location Estimators” by Dupray;        -   viii. U.S. Pat. No. 6,236,365 filed Jan. 22, 1999 entitled            “Location of a Mobile Station Using A Plurality Of            Commercial Infrastructures” by LeBlanc, Dupray and Karr;        -   ix. U.S. Pat. No. 6,235,365 filed: Apr. 23, 1999 entitled            “WIRELESS LOCATION USING MULTIPLE LOCATION ESTIMATORS” by            Dupray; and        -   x. International Patent Application No. PCT/US01/17957 filed            Jun. 4, 2001 entitled “A Wireless Location Gateway And            Applications Therefor” by Dupray; and    -   (b) if a primary wireless location technique fails (e.g., due to        an electronics malfunction), then assuming an alternative        technique is available that does not use, e.g., the        malfunctioning electronics of the primary technique, then the        alternative technique can be used for MS location.

However, the variety of wireless location techniques available is alsoproblematic for at least the following reasons:

-   -   (a) a request for an MS location can require either the        requester to know the wireless location service provider of the        geographical area where the MS is likely to be, or to contact a        location broker that is able to, e.g., determine a communication        network covering the geographical area within which the MS is        currently residing and activate (directly or through the MS's        wireless service provider) an appropriate wireless location        service. In the art, the technology enabling such a location        broker capability has been referred to as a “wireless location        gateway”. An embodiment of such a gateway is described in the        PCT/US97/15892 reference identified above;    -   (b) for communication networks relying on handset centric and/or        hybrid systems for MS location, MSs roaming from networks using        only network centric location capabilities will likely not have        the specialized electronics needed for being located, and        accordingly many location related network services will not be        available such as emergency services (e.g., E911 in the U.S.).    -   (c) different location techniques have different reliability and        accuracy characteristics. Thus, the wireless location technology        may need to be selected according to the requirements of the        location requesting application. For example, location        requesting applications that require relatively precise location        information are emergency rescue, and certain military related        applications (e.g., battlefield data fusion, battlefield        maneuvers and/or military command, control and communication        (C3)).

Accordingly, it would be desirable to integrate into a single wirelesslocation broker or wireless location gateway as many location techniquesas possible (or commercially feasible) so that location requests can befulfilled without the requester needing to know what location techniqueis used. It would be further desirable for roaming MSs to be able to belocated in coverage areas where a wireless location technique isdifferent from the one (or more) techniques supported in the primarysubscription area for the MS. Additionally, it would be desirable toprovide new applications for which MS location information can beapplied via, e.g., a wireless location gateway.

OBJECTS OF THE DISCLOSURE RELATING TO WIRELESS LOCATION

It is an objective of the present disclosure to provide a system andmethod for accurately locating people and/or objects in a cost effectivemanner wherein a location requester can obtain an MS location withoutneeding to provide location technique specific information with therequest.

It is a further object the present disclosure to provide wirelesslocation without the requester knowing the particulars of acommunication network with which the MS may be in contact, e.g., thecommercial radio service provider (CMRS), the wireless communicationsprotocol, etc. Furthermore, wireless location may be determined in twoor three spacial dimensions depending upon, e.g., the requirements ofthe location requesting application and the wireless locationtechnologies available in the area where the MS resides.

Yet another objective is to provide a low cost location system andmethod, adaptable to wireless telephony/Internet systems, for using aplurality of location techniques for increasing MS location accuracy andconsistency. In particular, the plurality of location techniques(embodied in “location estimators” also denoted “first order models” orFOMs herein) may be: activated according to any one or more of a numberof activation strategies such as: (i) concurrent activation (e.g., forobtaining two location estimates of an MS location), (ii) data-drivenactivation (e.g., activated when appropriate input data is available),(iii) priority activation (e.g., an attempt to activate a preferred FOMis first performed, and if unsuccessful, or a result unsatisfactory,then an attempt at activating a different second FOM is performed), (iv)“most recent location” (e.g., for obtaining the most recently determinedMS location).

Yet an other objective of the present disclosure is to provide, incombination with MS wireless location estimates, one or more of:

-   -   i. dimensional information such as an indication as to whether        the location is in two dimensions (e.g., generally corresponding        to a location on a two dimensional representation of a        geographical area) or three dimensions (e.g., additionally        having an elevation component corresponding to a floor in a high        rise building above or below the surrounding terrestrial        surface),    -   ii. timing information such as a timestamp indicative of when        the MS is presumed to have been at a corresponding estimated        location (e.g., generally, when corresponding wireless signal        measurements were first obtained),    -   iii. MS movement information such as velocity, direction of        movement, acceleration,    -   iv. performance information indicating, e.g., a likely accuracy        and/or reliability of the corresponding location estimate,        and/or likely variance in the location estimate (such variance        may be different along different dimensions, particularly        elevation), and/or status information indicative of success or        failure in locating the MS,    -   v. billing information indicating, e.g., a cost for the location        information and/or who is to be billed and/or itemizations of        discounts, taxes or tariffs for the wireless location service        performed,    -   vi. descriptive information as to who requested the location of        the MS,    -   vii. use permissions indicating who can access the MS location        estimate, e.g., there may be two MS location requests pending        for the same MS, once a location estimate is determined one of        the pending requests may be eligible for receiving the estimate        while the other is not, network statistics,    -   viii. descriptive information as to whether location enhancement        techniques were used such as snap an estimated MS location to a        nearest likely roadway (e.g., given an MS direction of travel,        speed and previous location estimates), and/or    -   ix. additional descriptive information such as identifying the        location techniques used, the priority given to determining the        MS location, the identity of location service provider(s) used        in determining the MS location.

Yet another object is to (or be able to) integrate into a wirelesslocation gateway a large number of MS location techniques such as:

-   -   (2.1) time-of-arrival wireless signal processing techniques;    -   (2.2) timing advance techniques (e.g., as provided in the GSM        wireless standard);    -   (2.2) time-difference-of-arrival wireless signal processing        techniques;    -   (2.3) adaptive wireless signal processing techniques having, for        example, learning capabilities and including, for instance,        artificial neural net and/or genetic algorithm processing;    -   (2.4) signal processing techniques for matching MS location        signals with wireless signal characteristics of known areas;    -   (2.5) conflict resolution techniques for resolving conflicts in        hypotheses for MS location estimates;    -   (2.6) techniques for enhancing MS location estimates through the        use of both heuristics and historical data associating MS        wireless signal characteristics with known locations and/or        environmental conditions;    -   (2.7) angle of arrival techniques (also denoted direction of        arrival) for estimating an angle and/or direction of wireless        signals transmitted from an MS;    -   (2.8) location techniques that use satellite signals such as GPS        signals received at the MS; e.g., network assisted GPS location        techniques, or non-network assisted GPS location techniques;    -   (2.9) wireless location techniques that use Doppler, phase        coherency, and other signal characteristics for determining MS        location, MS velocity and MS direction of movement;    -   (2.10) calibration techniques that utilize wireless signal        measurement survey data (e.g., signal measurements at verified        geographical locations) for adjusting or calibrating a wireless        location technique according to such survey data of a coverage        area;    -   (2.11) hybrid wireless location techniques that combine two or        more of the above location techniques (2.1)-(2.10) or other        wireless location techniques.

A related object is to integrate handset centric, network centric andhybrid systems so that the problems identified hereinabove aremitigated.

Note that it is a further objective of the present disclosure to providea “plug and play” capability for new wireless location estimators andwireless location requesting application, wherein new locationestimators and/or application can be easily incorporated into anembodiment of the present disclosure. For example, such plug and playcapability may include providing an interface that allows substantiallyautomatic integration of new FOMs, wherein such integration maybe at acentral site or at a mobile unit such as an MS. Regarding integrationinto a mobile unit, such a plug and play capability may be particularlyimportant in military contexts where data fusion may be required. Forexample, in a battlefield context it may be desirable to have arelatively small number of command units (mobile or otherwise) that arein contact with a higher level chain of command and/or providebattlefield analysis applications. However, if one or more of thecommand units (e.g., soldiers, tanks, helicopters, etc.) are disabled orotherwise are unable to properly communicate it may that softwareembodiments of wireless location technologies and/or certainapplications requiring wireless locations must be able to migratebetween the command units to thereby maintain appropriate battlefieldcommunications and/or combat coordination. More particularly, militaryapplications that, once provided with locations of friendly and enemyunits, analyze a global or overall view of a battlefield may becomputationally intensive enough so that it is not be practical to havesuch applications reside on every mobile unit, even though it may benecessary for such applications to migrate between mobile unitsaccording to casualties and other computational tasks and/or securityconstraints that can dynamically arise.

Yet another object is to provide novel applications for wirelesslocation that benefit from an integration of different locationtechniques.

Yet another object of the present disclosure is to provide a wirelessplatform that may be used substantially uniformly across a large numberof wireless applications, and in particular, wireless applications thatutilize wireless location.

Definitions

The following definitions are provided for convenience. In general, thedefinitions here are also defined elsewhere in this document as well.

(3.1) The term “wireless” herein is, in general, an abbreviation for“digital wireless”, and in particular, “wireless” refers to digitalradio signaling using one of standard digital protocols such as AdvancedMobile Phone Service (AMPS), Narrowband Advanced Mobile Phone ServiceCNAMPS), code division multiple access (CDMA) and Time Division MultipleAccess (TDMA), Global Systems Mobile (GSM), and time division multipleaccess (TDMA) as one skilled in the art will understand. However, otherwireless protocols are also within the scope of the present disclosurein that the disclosure is not dependent upon a particular wirelesssignaling convention. Additionally, it is intended that the scope of thedisclosure also encompass analog signal transmissions to the extentpermissible, and in some contexts may also include signals in bandwidthsother than radio such as optical and infrared.

(3.2) As used herein, the term “mobile station” (equivalently, MS)refers to a wireless device that is at least a transmitting device, andin most cases is also a wireless receiving device, such as a portableradio telephony handset. Note that in some contexts herein instead of,or in addition to, MS, the following terms are also used: “personalstation” (PS), and “location unit” (LU) or mobile unit. In general,these terms may be considered synonymous. Note that examples of variousMSs are identified in the Background section above.

(3.3) The terms, “wireless infrastructure” (or simply “infrastructure”),denotes one or more of: (a) a network for one or more of telephonycommunication services, (b) a collection of commonly controlledtransceivers for providing wireless communication with a plurality ofMSs, (c) the wireless Internet or portions thereof, (d) that portion ofcommunications network that receives and processes wirelesscommunications with wireless mobile stations. In particular, thisinfrastructure may in one embodiment include: (i) telephony wirelessbase stations (BS) such as those for radio mobile communication systemsbased on CDMA, AMPS, NAMPS, TDMA, and GSM wherein the base stationsprovide a network of cooperative communication channels with an airinterface to the MS, and (ii) a conventional telecommunicationsinterface with a Mobile Switch Center (MSC). Thus, an MS user within anarea serviced by the base stations may be provided with wirelesscommunication throughout the area by user transparent communicationtransfers (i.e., “handoffs”) between the user's MS and these basestations in order to maintain effective telephony service. The mobileswitch center (MSC) provides communications and control connectivityamong base stations and the public telephone network. Note that in somecontexts (e.g., military and/or emergency) at least some of the MSs mayalso provide base station capabilities such as receiving andtransmitting communications between two other MSs, e.g., wherein thesetwo other MSs may be out of range for communicating directly with oneanother.

(3.4) The phrase, “composite wireless signal characteristic values”denotes the result of aggregating and filtering a collection ofmeasurements of wireless signal samples, wherein these samples areobtained from the wireless communication between an MS to be located andthe base station infrastructure (e.g., a plurality of networked basestations). However, other phrases are also used herein to denote thiscollection of derived characteristic values depending on the context andthe likely orientation of the reader. For example, when viewing thesevalues from a wireless signal processing perspective of radioengineering, as in the descriptions of the subsequent DetailedDescription sections concerned with the aspects of the presentdisclosure for receiving MS signal measurements from the base stationinfrastructure, the phrase typically used is: “RF signal measurements”.Alternatively, from a data processing perspective, the phrases:“location signature cluster” and “location signal data” are used todescribe signal characteristic values between the MS and the pluralityof infrastructure base stations substantially simultaneously detectingMS transmissions. Moreover, since the location communications between anMS and the base station infrastructure typically include simultaneouscommunications with more than one base station, a related useful notionis that of a “location signature” (also denoted “loc sig” herein) whichis the composite wireless signal characteristic values for signalsamples between an MS (e.g., to be located) and a single base station.Also, in some contexts, the phrases: “signal characteristic values” or“signal characteristic data” are used when either or both a locationsignature(s) and/or a location signature cluster(s) are intended.

(3.5) The phrases “profile”, “subscriber profile”, and “user profile”,in general, will be used interchangeably. These phrases denote acollection of information residing on a network to which the usersubscribes or is registered to receive network services. In most cases,it is believed that a user will have such a network profile, wherein itmay include substantially any user information that is required to allowor prohibit access, activation, or fulfillment of one or more networkservices by the user, or by another user where the requested service bythe other user requires accessing information about the user that isidentified as being confidential or private.

SUMMARY DISCUSSION

The present disclosure relates to a method and system for performingwireless mobile station location and using resulting locations inservices provided to wireless subscribers. In one aspect, the presentdisclosure describes a wireless mobile station location computing methodand system that utilizes multiple wireless location computationalestimators (these estimators also denoted herein as MS locationhypothesizing computational models, “first order models”, FOMs, and/or“location estimating models”), for providing location estimates of atarget mobile station MS. Moreover, in the event that ambiguities and/orconflicts between the location estimates arise, such ambiguities and/orconflicts may be effectively and straightforwardly resolved. Moreover,the present disclosure provides a technique for calibrating theperformance of each of the location estimators so that a confidencevalue (e.g., a probability) can be assigned to each generated locationestimate. Additionally, the present disclosure provides astraightforward technique for using the confidence values (e.g.,probabilities) for deriving a resulting most likely location estimate ofa target wireless mobile station.

In one aspect, the present disclosure relates to a novel computationalmethod and architecture for synergistically combining the results of aplurality of computational models in a straightforward way that allowsthe models to be calibrated relative to one another so that differencesin results generated by the models can be readily resolved. Accordingly,the computational method and architecture of the present disclosure maybe applied to a wide range applications where synergies between multiplemodels is expected to be enhance performance.

In another more general aspect of the present disclosure, its multiplemodel gateway architecture may used for other application domains beyondwireless location. For example, application domains related toevaluating, diagnosing, monitoring and/or predicting a condition orstate of affairs in the application domain. For example, suchapplication domains can be in the areas of medical, electronic, and/ornetwork evaluation, diagnosis, monitoring and/or prediction. However,other application domains are within the scope of the disclosure.

To further elaborate, for a particular application domain and acorresponding particular application having access to a plurality ofcomputational models (each generating a hypothetical estimate orevaluation of a desired result(s) from/in a space of hypothesisresults), the present disclosure describes, at a high level, as anymethod or system that performs the following steps:

-   -   (4.1.1) A step of determining a classification scheme for        determining an input class (C) for each input data set obtained        for a condition or state of affairs to be evaluated by the        particular application, wherein this input data set (or portions        thereof) are to be supplied to the plurality of computational        models (FOMs). For determining each input class, there is a        range, RC, of a plurality of ranges, from a space (the        hypothesis space) of possible resulting hypotheses (or        evaluations) that could be output by the FOMs. The input data        sets of this input class C are identified as those input data        sets that are expected to have their corresponding desired        result(s), generated by the particular application, in the range        R.        -   Some examples will be illustrative. For a wireless location            system as the “particular application”, the present step, in            one embodiment, determines geographical subareas of a            wireless network coverage area that have “similar” wireless            signal characteristics. Such subareas may be relatively easy            to determine, and there may be no constraint on the size of            the subareas. The intention is to determine: (a) such a            subarea as only a general area where a target MS to be            located must reside, and (b) the subarea should be            relatively homogeneous regarding at least one wireless            signaling characteristic. Accordingly, in one embodiment of            the present step, (a) and (b) are believed to be            substantially satisfied by grouping together into the same            input class the wireless signal data sets (i.e., input data            sets) from corresponding target MS locations wherein at each            of the target MS locations: (i) the set of base stations            detected by the target MS (at the location) is substantially            the same, and/or (b) the set of base stations detecting the            target MS is substantially the same set of base stations.        -   Classification schemes in other application domains are also            within the scope of the present step. For example, in            diagnosis applications (e.g., medical, electronic, network,            electromechanical), symptoms (e.g., input data sets) are            generally classified according to their corresponding            diagnoses. Also, in automated or electronic scene, object or            image recognition such classification schemes may be used.        -   In some application domains, the present step may in viewed            as a pre-filter or pre-selection capability for reducing            subsequent computational overhead, e.g., so that only            appropriate FOMs are activated (such appropriateness may be            as much a function of economics and/or contractual            agreements as it is the input data set available and the            FOMs that are available).        -   Note for that more complex classifications, there are            numerous techniques and commercial packages for determining            such a classification scheme. In particular, the            statistically based system, “CART” (acronym for            Classification and Regression Trees) by ANGOSS Software            International Limited of Toronto, Canada is one such            package. Further, note that this step is intended to provide            reliable but not necessarily highly accurate ranges R for            the desired results. Also note that in some applications            there may be only a single input class. Accordingly, in this            latter case the present step may be omitted entirely.    -   (4.1.2) A step of calibrating each of the plurality of        computational models (FOMs) so that each subsequent hypothesis        generated by one of the models has a confidence value (e.g.,        probability or other measurement) associated therewith that is        indicative of the likeliness of the hypothesis being correct.        The calibrating of this step is performed using the classes of        the input classification scheme determined in the above step        (4.1.1). Note that there may be only a single class (such as if        step (4.1.1) were omitted). In one embodiment of present step,        each FOM is supplied with inputs from a given fixed input class,        wherein each of these inputs are for a known condition (or state        of affairs) and/or a condition that can be verified as to its        identity. In particular, the identity of the known condition        constitutes a “correct” hypothesis (i.e., a desired result) with        which outputs from FOMs can be compared and/or further        processed. Subsequently, the performance of each model is        determined for the input class and a confidence value is        assigned to the model for inputs received from the input class.        Note that this procedure is repeated with each input class        available from the input classification scheme. In performing        this procedure, an application domain specific criteria is used        to determine whether the hypotheses generated by the models        identify the desired results in the hypothesis space.        Accordingly, for each of the models, when supplied with an input        data set from a fixed input class, the hypothesis generated by        the model will be given the confidence value determined for this        input class as an indication of the likelihood of the generated        hypothesis being correct (i.e., the desired result). Note that        the confidence value for each generated hypothesis may be        computed as a probability that the hypothesis is correct.        -   Note that for a wireless location application, the criteria            (in one embodiment) is whether a location hypothesis            contains the actual location where the MS was when the            corresponding input data set (wireless signal measurements)            were communicated between this MS and the wireless network.        -   For applications related to the diagnosis of electronic            systems, this criteria may be whether an hypothesis            identifies a proper functional unit such as a circuit board            or chip.        -   For economic forecasting applications, this criteria may be            whether an hypothesis is within a particular range of the            correct hypothesis. For example, if an application according            to the present disclosure predicts the U.S. gross national            product (GNP) six months into the future according to            certain inputs (defining input data sets), then hypotheses            generated from historical data that has associated therewith            the actual corresponding GNP (six months later), may be used            for calibrating each of the plurality of economic            forecasting models (FOMs). Thus, the application specific            criteria for this case may be that a generated hypothesis is            within, say, 10% of the actual corresponding six month GNP            prediction.        -   For identifying a known object such as an air or space            borne, terrestrial vehicle, or watercraft, the criteria may            be whether an hypothesis actually identifies the object.        -   For geophysical analysis applications (e.g., for identifying            and/or classifying and/or mapping mineral deposits, oil,            aquifers or seismic faults), the criteria may be whether an            hypothesis provides a correct analysis.        -   Note that the applications described herein are            illustrative, but not comprehensive of the scope of the            present disclosure. Further note that this step typically is            performed at least once prior to inputting input data sets            whose resulting hypotheses are to be used to determine the            desired or correct results. Additionally, once an initial            calibration has been performed, this step may also be            performed: (a) intermittently between the generation of            hypotheses, and/or (b) substantially continuously and in            parallel with the generation of hypotheses by the models.    -   (4.1.3) A step of providing one or more input data sets to the        models (FOMs) for generating a plurality of hypotheses, wherein        the result(s) desired to be hypothesized are unknown. Moreover,        note that the generated hypotheses are preferred to have a same        data structure definition.        -   For example, for a wireless location system, the present            step provides an input data set including the composite            signal characteristic values to one or more MS location            hypothesizing computational models, wherein each such model            subsequently determines one or more initial estimates (also            denoted location hypotheses) of the location of the target            MS. Note that one or more of these model may be based on,            for example, the signal processing techniques 2.1 through            2.3 above.    -   (4.1.4) A step of adjusting or modifying the generated        hypotheses output by the models, wherein for such an hypothesis,        adjustments may be performed on one or both of its hypothesized        result H.R, and its confidence value for further enhancing the        performance of embodiments of the disclosure. In one embodiment        of this step, H.R is used as an index to retrieve other results        from an archival database, wherein this database associates        hypothesized results with their corresponding desired or correct        results. Thus, H.R may be used to identify data from other        archived hypothesized results that are “nearby” to H.R, and        subsequently use the nearby data to retrieve the corresponding        desired results. Thus, the set of retrieved desired results may        be used to define a new “adjusted” hypothesis.        -   For example, for a wireless location system utilizing the            present disclosure, each location hypothesis, H, identifies            an area for a target MS, and H can used to identify            additional related locations included in archived hypotheses            generated by the same FOM as generated H. For instance, such            related locations may be the area centroids of the archived            hypotheses, wherein these centroids reside within the area            hypothesized by H. Accordingly, such centroids may be used            to retrieve the corresponding actual verified MS locations            (i.e., the corresponding desired results), and these            retrieved verified locations may be used to generate a new            adjusted area that is likely to be more accurate than H. In            particular, a convex hull of the verified locations may be            used as a basis for determining a new location hypothesis of            the target MS. Moreover, this aspect of the present            disclosure may include the preprocessing of such adjustments            throughout a wireless coverage area to produce a geolocation            vector gradient field, wherein for each archived hypotheses            H (having L_(H) as an MS location estimate) for a designated            FOM, throughout the coverage area, a corresponding verified            location version VL_(H) is determined. Subsequently, the            adjustment vector AV_(H)=(VL_(H)−L_(H)) is determined as one            of the adjustment vectors of the vector gradient field.            Thus, L_(H) and AV_(H) are associated in the data archive as            a record of the vector gradient field. Accordingly, when a            location hypothesis H0 for a target MS at an unknown            location is generated (the hypothesis H0 having L0 as the            target MS location estimate), records within the vector            gradient field having their corresponding location L_(H)            “near” L0, (e.g., within area of a predetermined distance            about L0 or a “neighborhood: of L0) can be retrieved.            Accordingly, an adjustment to L0 can be determined as a            function of the L_(H) and AV_(H) values of the retrieved            records. Note that an adjustment to L0 may be simply an            average of these AV_(H) vectors for the retrieved records.            Alternatively, the AV_(H) values may be weighted such that            the AV_(H) having L_(H) closer to L0 are more influential in            the resulting derived location for the target MS. More            generally, the adjustment technique includes a method for            interpolating an adjustment at L0 from the verified            adjustments at locations about L0. Enhancements on such            adjustment/interpolation techniques are also within the            scope of the present disclosure. For example, the weightings            (or other terms of an such an interpolation technique) may            be combined with other known wireless signal characteristics            of the area such as an identification of: (a) a known sharp            change in the geolocation gradient vector field, and/or (b)            a subarea having reduced wireless transmission capabilities,            and/or (c) a subarea wherein the retrieved records for the            subarea have their estimates L_(H) widely spaced apart,            and/or (d) a subarea wherein there is an insufficient number            of retrieved records.        -   For other application domains, the present step requires a            first technique to determine both “nearby” archived data            from previously archived hypotheses, and a second technique            to determine an “adjusted” hypothesis from the retrieved            desired results. In general, such techniques can be            relatively straightforward to provide when the hypothesized            results reside in a vector space, and more particularly, in            a Cartesian product of the real numbers. Accordingly, there            are numerous applications that can be configured to generate            hypothesized results in a vector space (or Cartesian product            of the real numbers). For instance, economic financial            forecasting applications typically result in numeric            predictions where the first and second techniques can be,            e.g., substantially identical to the centroid and convex            hull techniques for the wireless location application.; and    -   (4.1.5) A step of subsequently computing a “most likely” target        MS location estimate is computed. for outputting to a location        requesting application such as 911 emergency, the fire or police        departments, taxi services, etc. Note that in computing the most        likely target MS location estimate a plurality of location        hypotheses may be taken into account. In fact, it is an        important aspect of the present disclosure that the most likely        MS location estimate is determined by computationally forming a        composite MS location estimate utilizing such a plurality of        location hypotheses so that, for example, location estimate        similarities between location hypotheses can be effectively        utilized.

Referring to (4.1.3) there may be hypotheses for estimating not onlydesired result(s), but also hypotheses may be generated that indicatewhere the desired result(s) is not. Thus, if the confidence values areprobabilities, an hypothesis may be generated that has a very low (nearzero) probability of having the desired result. As an aside, note thatin general, for each generated hypothesis, H, having a probability, P,there is a dual hypothesis H^(c) that may be generated, wherein theH^(c) represents the complementary hypothesis that the desired result isin the space of hypothesized results outside of H. Thus, the probabilitythat the desired result(s) is outside of the result hypothesized by H is1−P. Accordingly, with each location hypothesis having a probabilityfavorably indicating where a desired result may be (i.e., P>=0.5), thereis a corresponding probability for the complement hypothesis thatindicates where the desired result(s) is unlikely to be. Thus, applyingthis reasoning to a wireless location application utilizing the presentdisclosure, then for an hypothesis H indicating that the target MS is ina geographical area A, there is a dual location estimate H^(c) that maybe generated, wherein the H^(c) represents the area outside of A and theprobability that the target MS is outside of A is 1−P. Thus, with eachlocation hypothesis having a probability favorably indicating where atarget MS may be (i.e., P>=0.5), there is a corresponding probabilityfor the complement area not represented by the location hypothesis thatdoes not favor the target MS being in this complement area. Further,note that similar dual hypotheses can be used in other applicationsusing the multiple model architecture of the present disclosure whenprobabilities are assigned to hypotheses generated by the models of theapplication.

Referring to (4.1.3) as it relates to a wireless location systemprovided by the present disclosure, note that, it is an aspect of thepresent disclosure to provide location hypothesis enhancing andevaluation techniques that can adjust target MS location estimatesaccording to historical MS location data and/or adjust the confidencevalues of location hypotheses according to how consistent thecorresponding target MS location estimate is: (a) with historical MSsignal characteristic values, (b) with various physical constraints, and(c) with various heuristics. In particular, the following capabilitiesare provided by the present disclosure:

-   -   (5.1) a capability for enhancing the accuracy of an initial        location hypothesis, H, generated by a first order model,        FOM_(H), by using H as, essentially, a query or index into an        historical data base (denoted herein as the location signature        data base). Note, this data base may include: (a) a plurality of        previously obtained location signature clusters (i.e., composite        wireless signal characteristic values) such that for each such        cluster there is an associated actual or verified MS locations        where an MS communicated with the base station infrastructure        for locating the MS, and (b) previous MS location hypothesis        estimates from FOM_(H) derived from each of the location        signature clusters stored according to (a). Alternatively this        data base include a location error gradient field for the know        location errors for FOM_(H);    -   (5.2) a capability for analyzing composite signal characteristic        values of wireless communications between the target MS and the        base station infrastructure, wherein such values are compared        with composite signal characteristics values of known MS        locations (these latter values being archived in the location        signature data base). In one instance, the composite signal        characteristic values used to generate various location        hypotheses for the target MS are compared against wireless        signal data of known MS locations stored in the location        signature data base for determining the reliability of the        location hypothesizing models for particular geographic areas        and/or environmental conditions;    -   (5.3) a capability for reasoning about the likeliness of a        location hypothesis wherein this reasoning capability uses        heuristics and constraints based on physics and physical        properties of the location geography;    -   (5.4) an hypothesis generating capability for generating new        location hypotheses from previous hypotheses.

As also mentioned above in (2.3), the present embodiments of the presentdisclosure may utilize adaptive signal processing techniques. Oneparticularly important utilization of such techniques includes theautomatic tuning of such embodiments so that, e.g., such tuning can beapplied to adjusting the values of location processing system parametersthat affect the processing performed by such embodiments. For example,such system parameters as those used for determining the size of ageographical area to be specified when retrieving location signal dataof known MS locations from the historical (location signature) data basecan substantially affect the location processing. In particular, asystem parameter specifying a minimum size for such a geographical areamay, if too large, cause unnecessary inaccuracies in locating an MS.Accordingly, to accomplish a tuning of such system parameters, anadaptation engine is included in the present disclosure forautomatically adjusting or tuning parameters used by embodiments of thepresent disclosure. Note that in one embodiment, the adaptation engineis based on genetic algorithm techniques.

Embodiments of the present disclosure may include one or more FOMs thatmay be generally denoted as classification models wherein such FOMs aretrained or calibrated to associate particular composite wireless signalcharacteristic values with a geographical location where a target MScould likely generate the wireless signal samples from which thecomposite wireless signal characteristic values are derived. Further,embodiments of the present disclosure may include the capability fortraining and retraining such classification FOMs to automaticallymaintain the accuracy of these models even though substantial changes tothe radio coverage area may occur, such as the construction of a newhigh rise building or seasonal variations (due to, for example, foliagevariations). As used herein, “training” refers to iteratively presenting“training data” to a computational module for changing the behavior ofthe module so that the module may perform progressively better as itlearns appropriate behavioral responses to the training data.Accordingly, training may include, for example, the repeated input oftraining data to an artificial neural network, or repeated statisticalregression analyses on different and/or enhanced training data (e.g.,statistical sample data sets). Note that other embodiments of a trainedpattern matching FOMs for wireless location are disclosed in U.S. Pat.No. 6,026,304, titled “Radio Transmitter Location Finding for WirelessCommunication Network Services and Management,” filed Jan. 8, 1997 andissued Feb. 15, 2000, having Hilsenrath and Wax as inventors, thispatent being incorporated herein fully by reference.

It is well known in the wireless telephony art that the phenomenon ofsignal multipath and shadow fading renders most analytical locationcomputational techniques such as time-of-arrival (TOA) ortime-difference-of-arrival (TDOA) substantially error prone in urbanareas and particularly in dense urban areas without further statisticalcorrelation processing such as such super resolution as disclosed inU.S. Pat. No. 5,890,068 by Fattouche et. al. issued on Mar. 30, 1999 andincorporated fully herein by reference. Moreover, it may be the casethat even though such additional processing is performed, the multipathphenomenon may still be problematic. However, this same multipathphenomenon also may produce substantially distinct or peculiar signalmeasurement patterns, wherein such a pattern coincides with a relativelysmall geographical area. Thus, embodiments of the present disclosure mayinclude a FOM(s) that utilizes multipath as an advantage for increasingaccuracy. Moreover, it is worthwhile to note that the utilization ofclassification FOMs in high multipath environments is especiallyadvantageous in that high multipath environments are typically denselypopulated. Thus, since such environments are also capable of yielding agreater density of MS location signal data from MSs whose actuallocations can be obtained, there can be a substantial amount of trainingor calibration data captured by an embodiment of the present disclosurefor training or calibrating such classification FOMs and forprogressively improving the MS location accuracy of such models.

It is also an aspect of the present disclosure that classification FOMsmay be utilized that determine target MS locations by correlating and/orassociating network anomalous behavior with geographic locations wheresuch behavior occurs. That is, network behaviors that are problematicfor voice and/or data communication may be used advantageously forlocating a target MS. For example, it is well known that wirelessnetworks typically have within their coverage areas persistent subareaswhere voice quality is problematic due to, e.g., measurements related tohigh total errors, a high error rate, or change in error rate. Inparticular, such measurements may be related to frame error rates,redundancy errors, co-channel interference, excessive handoffs betweenbase stations, and/or other call quality measurements. Additionally,measurements may be used that are related to subareas where wirelesscommunication between the network and a target MS is not sufficient tomaintain a call (i.e., “deadzones”). Thus, information about such socalled problematic behaviors may used by, e.g., a location estimator(FOM) to generate a more accurate estimate of a target MS. For example,such network behavioral measurements may be provided for training anartificial neural network and/or for providing to a statisticalregression analysis technique and/or statistical prediction models(e.g., using principle decomposition, partial least squares, or otherregression techniques) for associating or correlating such measurementswith the geographic area for which they likely derive. Moreover, notethat such network behavioral measurements can also be used to reduce thelikelihood of a target MS being in an area if such measurements are notwhat would be expected for the area.

It is also an aspect of the present disclosure that FOMs themselves maybe hybrid combinations of MS location techniques. For example, anembodiment of the present disclosure may include a FOM that uses acombination of Time Difference of Arrival (TDOA) and Timing Advance (TA)location measurement techniques for locating the target MS, wherein sucha technique may require only minor modifications to the wirelessinfrastructure. In particular, such a FOM may provide reduced MSlocation errors and reduced resolution of ambiguities than are presentwhen these techniques are used separately. One embodiment of such a FOM(also denoted the Yost Model or FOM herein) is disclosed in U.S. Pat.No. 5,987,329 filed Jul. 30, 1997 and issued Nov. 16, 1999 titled:“System and Method for Mobile Telephone Location Measurement Using aHybrid Technique” having Yost and Panchapakesan as inventors, thispatent being fully incorporated herein by reference.

Additionally, note that FOMs related to the Yost Model may also beincorporated into embodiments of the present disclosure wherein anelliptical search restriction location technique may also be utilized.In particular, such a technique is disclosed in U.S. patent application,having U.S. Pat. No. 5,930,717, and titled: “System and Method UsingElliptical Search Area Coverage in Determining the Location of a MobileTerminal”, filed Jul. 30, 1997, by Yost et. al. which is also fullyincorporated by reference herein.

It is also a related aspect of the present disclosure to include aplurality of stationary, low cost, low power “location detection basestations” (LBS), each such LBS having both restricted range MS detectioncapabilities, and a built-in MS. Accordingly, a grid of such LBSs can beutilized for providing wireless signaling characteristic data (fromtheir built-in MSs) for: (a) (re)training such classification FOMs, and(b) calibrating the FOMs so that each generated location hypothesis hasa reliable confidence value (e.g., probability) indicative of thelikeliness of the target MS being in an area represented by the locationhypothesis.

It is a further aspect of the present disclosure that the personalcommunication system (PCS) infrastructures currently being developed bytelecommunication providers offer an appropriate localizedinfrastructure base upon which to build various personal locationsystems (PLS) employing the present disclosure and/or utilizing thetechniques disclosed herein. In particular, the present disclosure isespecially suitable for the location of people and/or objects using codedivision multiple access (CDMA) wireless infrastructures, although otherwireless infrastructures, such as, time division multiple access (TDMA)infrastructures and GSM are also contemplated. CDMA general principlesare described, for example, in U.S. Pat. No. 5,109,390, to Gilhausen, etal, which is also incorporated herein by reference.

As mentioned in (1.7) and in the discussion of classification FOMsabove, embodiments of the present disclosure may include components(e.g., FOMs) that can substantially automatically retrain themselves tocompensate for variations in wireless signal characteristics (e.g.,multipath) due to environmental and/or topographic changes to ageographic area serviced by an embodiment of the present disclosure. Forexample, in one embodiment, the present disclosure optionally includeslow cost, low power base stations, denoted location base stations (LBS)above, providing, for example, CDMA pilot channels to a very limitedarea about each such LBS. The location base stations may provide limitedvoice traffic capabilities, but each is capable of gathering sufficientwireless signal characteristics from an MS within the location basestation's range to facilitate locating the MS. Thus, by positioning thelocation base stations at known locations in a geographic region suchas, for instance, on street lamp poles and road signs, additional MSlocation accuracy can be obtained. That is, due to the low power signaloutput by such location base stations, for there to be signaling controlcommunication (e.g., pilot signaling and other control signals) betweena location base station and a target MS, the MS must be relatively nearthe location base station. Additionally, for each location base stationnot in communication with the target MS, it is likely that the MS is notnear to this location base station. Thus, by utilizing informationreceived from both location base stations in communication with thetarget MS and those that are not in communication with the target MS, anembodiment of the present disclosure may substantially narrow thepossible geographic areas within which the target MS is likely to be.Further, by providing each location base station (LBS) with a co-locatedstationary wireless transceiver (denoted a built-in MS above) havingsimilar functionality to an MS, the following advantages are provided:

(6.1) assuming that the co-located base station capabilities and thestationary transceiver of an LBS are such that the base stationcapabilities and the stationary transceiver communicate with oneanother, the stationary transceiver can be signaled by anothercomponent(s) of the present disclosure to activate or deactivate itsassociated base station capability, thereby conserving power for the LBSthat operates on a restricted power such as solar electrical power;

(6.2) the stationary transceiver of an LBS can be used for transferringtarget MS location information obtained by the LBS to a conventionaltelephony base station;

6.3) since the location of each LBS is known and can be used in locationprocessing, an embodiment of the present disclosure is able to (re)trainitself in geographical areas having such LBSs. That is, by activatingeach LBS stationary transceiver so that there is signal communicationbetween the stationary transceiver and surrounding base stations withinrange, wireless signal characteristic values for the location of thestationary transceiver are obtained for each such base station.Accordingly, such characteristic values can then be associated with theknown location of the stationary transceiver for training various of thelocation processing modules of the present disclosure such as theclassification FOMs discussed above. In particular, such training and/orcalibrating may include:

(i) (re)training FOMs;

(ii) adjusting the confidence value initially assigned to a locationhypothesis according to how accurate the generating FOM is in estimatingthe location of the stationary transceiver using data obtained fromwireless signal characteristics of signals between the stationarytransceiver and base stations with which the stationary transceiver iscapable of communicating;

(iii) automatically updating the previously mentioned historical database (i.e., the location signature data base), wherein the stored signalcharacteristic data for each stationary transceiver can be used fordetecting environmental and/or topographical changes (e.g., a newlybuilt high rise or other structures capable of altering the multipathcharacteristics of a given geographical area); and

(iv) tuning of the location system parameters, wherein the steps of: (a)modifying various system parameters and (b) testing the performance ofthe modified location system on verified mobile station location data(including the stationary transceiver signal characteristic data), thesesteps being interleaved and repeatedly performed for obtaining bettersystem location accuracy within useful time constraints.

One embodiment of the present disclosure utilizes a mobile (location)base station (MBS) that can be, for example, incorporated into avehicle, such as an ambulance, police car, or taxi. Such a vehicle cantravel to sites having a transmitting target MS, wherein such sites maybe randomly located and the signal characteristic data from thetransmitting target MS at such a location can consequently be archivedwith a verified location measurement performed at the site by the mobilelocation base station. Moreover, it is important to note that such amobile location base station as its name implies also includes basestation electronics for communicating with mobile stations, though notnecessarily in the manner of a conventional infrastructure base station.In particular, a mobile location base station may (in one embodiment)only monitor signal characteristics, such as MS signal strength, from atarget MS without transmitting signals to the target MS. Alternatively,a mobile location base station can periodically be in bi-directionalcommunication with a target MS for determining a signal time-of-arrival(or time-difference-of-arrival) measurement between the mobile locationbase station and the target MS. Additionally, each such mobile locationbase station includes components for estimating the location of themobile location base station, such mobile location base station locationestimates being important when the mobile location base station is usedfor locating a target MS via, for example, time-of-arrival ortime-difference-of-arrival measurements as one skilled in the art willappreciate. In particular, a mobile location base station can include:

(7.1) a mobile station (MS) for both communicating with other componentsof the present disclosure (such as a location processing center of thepresent disclosure);

(7.2) a GPS receiver for determining a location of the mobile locationbase station;

(7.3) a gyroscope and other dead reckoning devices; and

(7.4) devices for operator manual entry of a mobile location basestation location.

Furthermore, a mobile location base station includes modules forintegrating or reconciling distinct mobile location base stationlocation estimates that, for example, can be obtained using thecomponents and devices of (7.1) through (7.4) above. That is, locationestimates for the mobile location base station may be obtained from: GPSsatellite data, mobile location base station data provided by thelocation processing center, dead reckoning data obtained from the mobilelocation base station vehicle dead reckoning devices, and location datamanually input by an operator of the mobile location base station.

The location estimating system of the present disclosure offers manyadvantages over existing location systems. The present disclosureemploys a number of distinctly different location estimators whichprovide a greater degree of accuracy and/or reliability than is possiblewith existing wireless location systems. For instance, the locationmodels provided may include not only the radius-radius/TOA and TDOAtechniques but also adaptive techniques such as artificial neural nettechniques and the techniques disclosed in the U.S. Pat. No. 6,026,304by Hilsenrath et. al. incorporated fully by reference herein, and angleor direction of arrival techniques as well as substantially any otherwireless location technique wherein appropriate input data can beobtained. Note that hybrid location estimators based on combinations ofsuch techniques (such as the location technique of U.S. Pat. No.5,987,329 by Yost et. al.) may also be provided by embodiments of thepresent disclosure.

It is also an aspect of the present disclosure that various embodimentsmay provide various strategies for activating, within a single MSlocation instance, one or more location estimators (FOMs), wherein eachsuch activated location estimator is provided with sufficient wirelesssignal data input for the activation. In one embodiment, one suchstrategy may be called “greedy” in that substantially as many locationestimators may be activated as there is sufficient input (additionally,time and resources as well) for activation. Note that some wirelesslocation techniques are dependent on specialized location relateddevices being operational such as fixed or network based receivers,antennas, transceivers, and/or signal processing equipment. Additionallynote that some location techniques also require particular functionalityto be operable in the MS; e.g., functionality for detecting one or morelocation related signals from satellites (more generally non-terrestrialtransmitting stations). For example, the signals may be GPS signals.Accordingly, certain wireless location techniques may have theiractivations dependent upon whether such location related devices and/orMS functionality are available and operable for each instance ofdetermining an MS location. Thus, for each MS wireless locationinstance, location estimators may be activated according to the operablefeatures present during an MS location instance for providing inputactivation data.

Embodiments of the present disclosure may be able to adapt toenvironmental changes substantially as frequently as desired. Thus, anembodiment of the present disclosure may be able to take into accountchanges in the location topography over time without extensive manualdata manipulation. Moreover, the present disclosure can be utilized withvarying amounts of signal measurement inputs. Thus, if a locationestimate is desired in a very short time interval (e.g., less thanapproximately one to two seconds), then the present disclosure can beused with only as much signal measurement data as is possible to acquireduring an initial portion of this time interval. Subsequently, after agreater amount of signal measurement data has been acquired, additionalmore accurate location estimates may be obtained. Note that thiscapability can be useful in the context of 911 emergency response inthat a first quick coarse wireless mobile station location estimate canbe used to route a 911 call from the mobile station to a 911 emergencyresponse center that has responsibility for the area containing themobile station and the 911 caller. Subsequently, once the 911 call hasbeen routed according to this first quick location estimate, bycontinuing to receive additional wireless signal measurements, morereliable and accurate location estimates of the mobile station can beobtained.

Moreover, there are numerous additional advantages of the system of thepresent disclosure when applied in communication systems using, e.g.,CDMA. The location system of the present disclosure readily benefitsfrom the distinct advantages of the CDMA spread spectrum scheme. Namely,these advantages include the exploitation of radio frequency spectralefficiency and isolation by (a) monitoring voice activity, (b)management of two-way power control, (c) provisioning of advancedvariable-rate modems and error correcting signal encoding, (d) inherentresistance to fading, (e) enhanced privacy, and (f) multiple “rake”digital data receivers and searcher receivers for correlation of signalmultipaths.

At a more general level, it is an aspect of the present disclosure todemonstrate the utilization of various novel computational paradigmssuch as:

(8.1) providing a multiple FOM computational architecture (asillustrated in FIG. 8) wherein:

-   -   (8.1.1) the hypotheses may be generated by modular independent        hypothesizing computational models (FOMs), wherein the FOMs have        been calibrated to thereby output confidence values        (probabilities) related to the likelihood of correspondingly        generated hypotheses being correct;    -   (8.1.2) the location hypotheses from the FOMs may be further        processed using additional amounts of application specific        processing common or generic to a plurality of the FOMs;    -   (8.1.3) the computational architecture may enhance the        hypotheses generated by the FOMs both according to past        performance of the models and according to application specific        constraints and heuristics without requiring complex feedback        loops for recalibrating one or more of the FOMs;    -   (8.1.4) the FOMs are relatively easily integrated into, modified        and extracted from the computational architecture; and        (8.2) providing a computational paradigm for enhancing an        initial estimated solution to a problem by using this initial        estimated solution as, effectively, a query or index into an        historical data base of previous solution estimates and        corresponding actual solutions for deriving an enhanced solution        estimate based on past performance of the module that generated        the initial estimated solution.

The multiple FOM architecture provided herein is useful in implementingsolutions in a wide range of applications. In fact, most of the DetailedDescription hereinbelow can be immediately translated into otherapplication areas, as one skilled in the art of computer applicationarchitectures will come to appreciate. For example, the followingadditional applications are within the scope of the present disclosure:

(9.1) document scanning applications;

(9.2) diagnosis and monitoring applications such as medicaldiagnosis/monitoring, communication network diagnosis/monitoring. Notethat in many cases, the domain wherein a diagnosis is to be performedhas a canonical hierarchical order among the components within thedomain. For example, in automobile diagnosis, the components of an automay be hierarchically ordered according to ease of replacement incombination within function. Thus, within an auto's electrical system(function), there may be a fuse box, and within the fuse box there willbe fuses. Thus, these components may be ordered as follows (highest tolowest): auto, electrical system, fuse box, fuses. Thus, if differentdiagnostic FOMs provided different hypotheses as to a problem with anauto, the confidence values for each component and its subcomponentsmaybe summed together to provide a likelihood value that the problemwithin the component. Accordingly, the lowest component having, forexample, at least a minimum threshold of summed confidences can beselected as the most likely component for either further analysis and/orreplacement. Note that such summed confidences may be normalized bydividing by the number of hypotheses generated from the same input sothat the highest summed confidence is one and the lowest is zero.Further note that this example is merely representative of a number ofdifferent diagnosis and/or prediction applications to which the presentdisclosure is applicable, wherein there are components that havecanonical hierarchical decompositions. For example, a technique similarto the auto illustration above may be provided for the diagnosis ofcomputer systems, networks (LANs, WANs, Internet and telephonynetworks), medical diagnosis from, e.g., x-rays, MRIs, sonograms, etc;

(9.3) robotics applications such as scene and/or object recognition.That is, various FOMs may process visual image input differently, and itmay be that for expediency, an object is recognized if the summedconfidence values for the object being recognized is above a certainthreshold;

(9.4) seismic and/or geologic signal processing applications such as forlocating oil and gas deposits;

(9.5) recognition of terrestrial and/or airborne objects fromsatellites, wherein there may be various spectral bands monitored.

(9.6) modeling of physical phenomena such as for assessing models ofmotion of physical phenomena through a fluid, wherein such motion causesan acoustic signal that traverses an uncertain path, and which isreceived by sensors with uncertain biases, in the presence of noise. Anexample of such modeling using a multiple hypothesis architecture isdisclosed in U.S. Pat. No. 6,304,833, filed Apr. 27, 1999 by Ferkinhoff,et al. and incorporated fully herein by reference.

(9.7) Additionally, note that this architecture need not have allmodules co-located. In particular, it is an additional aspect of thepresent disclosure that various modules can be remotely located from oneanother and communicate with one another via telecommunicationtransmissions such as telephony technologies (ISDN, virtual privatenetworks, POTS, DSL, etc.) and/or the Internet. Accordingly, the presentdisclosure is particularly adaptable to such distributed computingenvironments. For example, some number of the first order models mayreside in remote locations and communicate their generated hypothesesvia the Internet.

In an alternative embodiment of the present disclosure, the processingfollowing the generation of location hypotheses (each having an initiallocation estimate) by the first order models may be such that thisprocessing can be provided on Internet user nodes and the first ordermodels may reside at various Internet server sites. In thisconfiguration, an Internet user may request hypotheses from such remotefirst order models and perform the remaining processing at his/her node.Moreover, embodiments of the present disclosure may access FOMs at sitesdistributed on other communication networks such as a local area networkin a hotel, or an ad hoc network in a battlefield, military or emergencyscenario.

Additionally, note that it is within the scope of the present disclosureto provide one or more central location development or repository sitesthat may be networked to, for example, geographically dispersed locationcenters providing location services according to the present disclosure,wherein the FOMs may be accessed, substituted, enhanced or removeddynamically via network connections (via, e.g., the Internet or othernetwork) with a central location development or repository site. Thus, asmall but rapidly growing municipality in substantially flat low densityarea might initially be provided with access to, for example, two orthree FOMs for generating location hypotheses in the municipality'srelatively uncluttered radio signaling environment. However, as thepopulation density increases and the radio signaling environment becomescluttered by, for example, thermal noise and multipath, additional oralternative FOMs may be transferred via the network to the locationcenter for the municipality.

Note that in some embodiments of the present disclosure, since there maybe a lack of sequencing between the FOMs and subsequent processing ofhypotheses (e.g., location hypotheses, or other application specifichypotheses), the FOMs can be incorporated into an expert system, oranother computational architecture for performing “intelligent”processing if desired. For example, for an expert system architecture,each FOM may be activated from an antecedent of an expert system rule.Thus, the antecedent for such a rule can evaluate to TRUE if the FOMoutputs a location hypothesis, and the consequent portion of such a rulemay put the output location hypothesis on a list of location hypothesesoccurring in a particular time window for subsequent processing by thelocation center. Alternatively, activation of the FOMs may be in theconsequents of such expert system rules. That is, the antecedent of suchan expert system rule may determine if the conditions are appropriatefor invoking the FOM(s) in the rule's consequent.

Embodiments of the present disclosure may also be configured as ablackboard system with intelligent agents (FOMs). In this embodiment,each of the intelligent agents is calibrated using archived data so thatfor each of the input data sets provided either directly to theintelligent agents or to the blackboard, each hypothesis generated andplaced on the blackboard by the intelligent agents has a correspondingconfidence value indicative of an expected validity of the hypothesis.

Of course, other software architectures may also to used in implementingthe processing of the location center without departing from scope ofthe present disclosure. In particular, object-oriented architectures arealso within the scope of the present disclosure. For example, the FOMsmay be object methods on an MS location estimator object, wherein theestimator object receives substantially all target MS location signaldata output by the signal filtering subsystem. Alternatively, softwarebus architectures are contemplated by the present disclosure, as oneskilled in the art will understand, wherein the software architecturemay be modular and facilitate parallel processing.

Wireless Application Platform Services and Architecture

It is yet another aspect of the present disclosure to provide a platformor architecture for providing wireless application services to wirelesssubscribers. In particular, the present disclosure includes a serviceproviding platform that is substantially uniform over a plurality ofdifferent wireless application services, and in particular wirelesslocation based services, and/or, short and/or instant messagingservices, and in particularly in combination with Internet access forsuch services as mobile commerce (also known as “mcommerce”), personalcommunications with friends and family, wireless games, wirelessassessment of an emergency situation (e.g., where voice data, picturedata. e.g., from camera phones, is obtained, as well as datatransmissions from on-site emergency assessment and/or analysisequipment such as chemical analyzers, radiation analyzers, biochemicalhazard analyzers, etc.). Accordingly, this platform may be considered asa wireless location application hub, wherein a single instance (orsubstantially duplicate copies) of the platform can provide a pluralityof different wireless services to wireless subscribers. In particular,such a platform can provide robust generic wireless data communicationcapabilities that are required or desirable by a wide variety ofwireless application services, and particularly services using wirelesslocation capabilities. For example, such data communication capabilitiesprovided by such a platform can include:

-   -   (a) user profile processing: E.g., (i) using user profile        information for identifying and/or predicting information that        is likely to be of interest to the user; (ii) gathering user        profile information from not only receiving such profile        information from the user, but also performing data mining        operations on various public data sources for obtaining further        user profile information about specific users as well as more        general demographic profile information, and (iii) maintaining        limitations or constraints on the content and/or types of        information that can be stored in the user's profile.    -   (b) data encryption processing: E.g., encryption/decryption of a        user's personal profile, encryption/decryption of a user's        location (e.g., such user location encryption may be        particularly advantageous in a user witness protection program).    -   (c) data privacy processing: E.g., there may be only certain        individuals or designated agents that can view and/or modify a        user's profile; additionally, there may be certain portions of a        user's profile that can not be accessed without appropriate        permissions (e.g., financial information, home address, social        security number, etc.). Thus, various profile data items can be        grouped together, wherein each such group may be provided with        corresponding access permissions and/or restrictions. For        example, there may be a first group of data items that can be        accessed with substantially all access privileges of the user.        Individuals and/or designated agents having this access may        include: parents (e.g., where the user is under the age of say,        15), children of elderly parents. Optionally, a second smaller        group of profile data items may include, e.g., some financial        information, social security number, and other user        identifications, wherein individuals and/or designated agents        having access to this second group may include a spouse and/or        close family members. Optionally, a third group of profile data        items may include: professional and/or some personal information        that would be useful for a designated corporate agent that is,        e.g., subsidizing the use of the mobile station. Such a        corporate agent may be, e.g., the user's employer. Accordingly,        the user's employer may be allowed to view mobile station use        records, as well as modify restrictions on the services that can        be accessed via the mobile station (e.g., Internet transmission        of full length movies or other pay per view services). Other        groupings of profile data items are, of course, possible such as        a fourth grouping of user profile information related to        personal or professional commercial transactions that the user        may desire to perform, e.g., buy/sell a car, bicycle, or pair of        shoes, buy/sell tickets to a particular event (sports event or        other entertainment), buy/sell travel accommodations. Note that        the fourth group may be only viewed by pre-authorized or        pre-qualified agents, such as those identified individually        and/or aggregately by the user or a user designated agent (such        as an agent for an electronic yellow pages enterprise, an        Internet search service, and/or an Internet product discounter).        Optionally, another grouping of profile data items may be for an        organization to which the user is affiliated such as a        professional organization (e.g., American Medical Association,        American Bar Association, or other professional organization).        There may be other profile data groups for religious, personal,        and/or political organization user affiliations with correspond        access privileges and restrictions.    -   (d) data exposure processing: E.g., for various inquiries for        information about a user, the user may provide criteria about        what information may be exposed. Thus, for an anonymous inquiry        received due to, e.g., the location of the user, the user may        provide criteria for exposing certain interests such as        interests in cars, types of music, etc. Note the processing here        may be similar to that of the data privacy above, and in some        embodiments may be substantial identical therewith. However, if        sophisticated profile capabilities are accessible to mobile        station users, inconsistencies can occur within such a profile        wherein the user wishes to leave his/her profile groups        unaltered, but still exercise additional control such as exclude        all accesses from a particular person, and/or exclude all        accesses for a particular period of time, and/or provide access        to particular profile data items for a particular time period or        when the user is in a particular geographical location and/or        when the accessing agent is in a particular geographical        location or relationship to the user's geographical location.        Thus, in one embodiment, the data exposure processing        contemplated here may be a more dynamic version of the data        privacy processing above, wherein, e.g., user location, time        periods, and/or accessing agent location may be taken into        account. Additionally and/or alternatively, the data exposure        processing contemplated here may function as a profile access        supervisor or controller that can override (temporarily or until        countermanding input is provided) more stable long term profile        access criteria such as the profile data groups and their        corresponding access privileges and/or restrictions described        above.        -   Note that in one embodiment of the present disclosure, a            network service provider or other authorized agent may            provide predetermined groups of profile data together with            corresponding access permissions/restrictions that allow the            user to easily construct profile data groups (with their            corresponding access permissions/restrictions) and assign            individuals and/or categories of entities to such groups.            Thus, the user may provide network input to create the            first, second and fourth data profile groups described            above. Subsequently, the user may be able to exclude all            profile access by a particular organization, individual or            business without the user modifying the profile groupings.        -   It is important to note here that in the term “access” as            used regarding profile data not only encompasses the            discovery of such information by network agents that may            actively search user profiles for particular types of            information, but also encompasses the active exposure of            such profile data to selected enterprises, organizations,            and/or individuals. In particular, a network service            provider or other authorized agent may be granted permission            to distribute portions of the user's profile to certain            entities. For example, a user may request that his/her            profile include information that he/she wishes to purchase            various brand names of expensive clothing, but only when            these brands are on sale. Thus, such profile information may            be actively distributed to selected businesses.    -   (e) constraint checking and rule activation processing: E.g.,        evaluating application specific conditions in a substantially        uniform manner across a plurality of different applications        according to, e.g., data stored in a constraint database(s), a        rule base(s), and/or a user profile database(s)),    -   (f) transaction processing: for certain wireless applications        transaction based user interactions are most appropriate wherein        there is the ability to initiate, commit, and roll back or undo        a series of data communications as one skilled in the art will        understand. Moreover, it is desirable that such a transaction        processing capability provide for multilevel transactions        wherein one instance of a transaction can be within another,    -   (g) data synchronization: e.g., providing a duplicate copy of a        collection of data from one point on a communications network to        another point on the network,    -   (h) event or transaction logging: e.g., for some wireless        applications the interactions with users are sufficiently        important to warrant storing a trace of such interactions,    -   (i) common interfaces: e.g., substantially uniform interfaces        between an embodiment of the wireless application platform of        the present disclosure and both a plurality of wireless        applications as well as users of such applications,    -   (j) wireless location request triggering mechanisms: e.g., (i)        for requesting the information related to users of nearby        wireless mobiles when the requesting user is at a particular        location or area (e.g., at a ski resort, walking through a        downtown area), or at a particular time of day; or (ii) for        requesting periodic locations of persons (e.g., employees,        salespersons, friends, relatives, etc) or assets (e.g., a        furniture shipment), or sensitive materials (e.g., toxic wastes        being transported across country), or (iii) providing wireless        advertising or purchasing incentives.

Moreover, an application platform according to the present disclosuremay support such service functions as (a)-(j) immediately above viastandard telephony and/or network functionality including WAP,BlueTooth, WIFI, and other wireless (and wired) application protocols.It is important to note that the term WAP is being used generically torefer to any wireless Internet protocol, including HDML and any futurewireless Internet protocols that may be developed. The followingexamples are provided of some competing technologies that for thepurposes of the present description will be referred to generically asWAP. For instance, Web content may be delivered as existing HTML.However, Internet content may be provided wirelessly as proposed bySpyglass'Prism technology or i-mode which is popular in Japan. As afurther example, Internet content can be processed through a templatemodel that reads existing HTML content and fits the data to a templateoptimized for various types of wireless mobile stations such as thesystem proposed by Everypath.com. As another example, the data contentcan be delivered to a Palm Pilot or other PDA or handheld device thatuses a proprietary protocol. Thus it is an aspect of the presentdisclosure to provide an inventive wireless application platform whereinapplications can be substantially implemented by providing applicationspecific data which can be used to drive the application processingperformed by, e.g., the above listed functions.

It is important to note that the platform of the present disclosure isparticularly useful for cost effectively and quickly making “complex”network services available to subscribers; e.g., network services thatrequire far more additional network coordination and communicationbetween various network components (of one or more different networks)than services such as voice and data communication, and variousenhancements to these basic services. For example, for wireless locationbased services, at least the following network services and componentsmust communicate appropriately for performing at least some of thefollowing functions: (i) wireless signal measurements related to thelocation of a target MS must be captured and routed to a wirelesslocation entity for determining a location estimate of the target mobilestation; (ii) a component such as a wireless location gateway, mustdetermine what wireless location technology to activate to determine thetarget mobile station's location; moreover, such a determination islikely dependent upon the capabilities of the target mobile station,capabilities of wireless network (e.g., the wireless carrier with whichthe target mobile station is currently communicating) to supportparticular wireless location technologies, and/or the ability of thewireless carrier to communicate with particular wireless locationservice provider; (iii) billing for determining the location estimatemust be determined; (iv) a location request may be received from varioussources; (v) privacy and/or security issues must be resolved; (vi)location data representations may need to be resolved between a wirelesslocation providing service and a location based application; (vii) acapability for iteratively frequently performing such a wirelesslocation may be required, and appropriate network provisioning allocatedthereto such as in tracking a mobile station; (viii) wireless locationsmay require a verification capability such as a callback mechanism asdescribed in International Patent Application PCT/US00/40989 titled“Geographically Constrained Network Services”, filed Sep. 25, 2000 byGoldberg and Dupray and having International Publication No. WO02/00316, this reference being fully incorporated herein by reference;(ix) the location based application's output be may media rich in thesense that graphical and/or image representations may need tocommunicated to the user and/or to another network destination; thus,network congestion may occur due to increased network bandwidthrequired; (x) a wireless location based application may be only anintermediate step in enabling another application; e.g., in theInternational Patent Application by Goldberg and Dupray cited above, awireless location verification application may be performed prior to awireless network financial transaction such as a wireless gaming wagerto assure that the subscriber is in a location that allows such, or adownload of a geographically restricted software product (e.g., asoftware product that can only be downloaded and/or utilized in aparticular geographical region or country such as the U.S. or Canada dueto, for instance, national security concerns and/or patent violation, orother legal violations on the use of the software outside of theparticular area); (xi) location based games are popular in some areas,and such games may also utilize short messaging services (SMS); thus,coordination and communication between the game application, a wirelesslocation service provider, and the SMS provider must be performed; (xii)it is generally perceived that location based advertising is viewed withdistain by subscribers since such advertising has been not much morethan a location based broadcast vehicle for advertising; accordingly,what is believed desired is an “intelligent” location based advertisingcapability such as is disclosed herein and in International PatentApplication No. PCT/US01/17957 filed Jun. 4, 2001 entitled “A WirelessLocation Gateway And Applications Therefor” by Dupray incorporatedherein fully by reference; however, such intelligence may likely requireadditional complexity such as accessing subscriber profiles, activatingnetwork triggering mechanisms or network daemons or intelligentsubscriber network software agents to determine when and/or where asubscriber request is satisfied such as a request for obtaining ticketsto a local sporting event that is sold out.

It has been suggested that the most commercially viable location basedservices have yet to be determined, and that in order to determine suchservices, numerous location based applications will have to be developedand marketed to gain experience in what services subscribers will payfor and to provide subscribers with experience in using such services.However, due to the complexity of developing applications for suchservices, if a generic or uniform platform such as is provided by thepresent disclosure is not utilized, the overhead and financial risk indeveloping such services may be beyond the financial risk tolerance aswell as the technical expertise of wireless carriers and/or third partynetwork service developers to surmount. Various examples of complexnetwork services have been developed and/or described in the relevantart. For example, U.S. Pat. No. 5,742,905 by Peppe et. al. filed Sep.19, 1994, titled “Personal Communications Internetworking” fullyincorporated herein by reference discloses:

-   -   “a personal communications internetwork providing a network        subscriber with the ability to remotely control the receipt and        delivery of wireless and wireline voice and text messages. The        network operates as an interface between various wireless and        wireline networks, and also performs media translation, where        necessary. The subscriber's message receipt and delivery options        are maintained in a database which the subscriber may access by        wireless or wireline communications to update the options        programmed in the database. The subscriber may be provided with        CallCommand service which provides real-time control of voice        calls while using a wireless data terminal or PDA.”        As a further example, International Patent Application        PCT/IB00/01995 Jhanji having International Publication No. WO        144998 and titled “IMPROVED SYSTEMS FOR COMMUNICATING CURRENT        AND FUTURE ACTIVITY INFORMATION AMONG MOBILE INTERNET USERS AND        METHODS THEREFOR” is fully incorporated herein by reference,        wherein this application discloses:    -   “there is provided a search facility wherein a user may search        among all users and/or posted information (or at least users        and/or information to which the searcher has access privilege)        for postings or users based on some search criteria. Since        substantially all user profiles and posted information are kept        in the database subsystem, such data is available to those,        having the proper access privilege. By way of example, a certain        user may perform a search among selected ones of her friends for        those currently engaged in shopping activities or planning to go        shopping. As another example, a certain user may perform a        search to check on the status, location, or activity pertaining        to a specific other user. As another example, a given user may        wish to search for anyone in the public who is interested in a        particular activity, who may be in a particular location, or who        may have a certain profile characteristic of interest. Since        many of the items of information pertaining to user activities        are time-sensitive, searches preferably take into account the        time component whenever appropriate (e.g., for activity        currently taking place or proposed in the future). Along with        user profile and activity, the invention permits users to find        one another based on location and time, as well as having a        degree of control over the privacy of their user profile and        posted information.”

However, it is believed that most commercially viable complex networkservices have yet to be developed, and the present disclosure isdirected to both such novel new network services, and a method andsystem for rapidly providing such services to subscribers, wherein theapplications providing such services use various combinations of, e.g.,SMS, MS location services/applications, email services/applications,voice and data transmission services/applications, Internet access,Internet accessible applications, and/or voice over IPservices/applications.

Moreover, note that the network services platform of the presentdisclosure may also be utilized to expedite providing other subscriberservices, complex or otherwise. For example, “intelligent” electronicyellow page capabilities may require capabilities such as (xii) aboveregardless of whether such capabilities include a location basedcomponent.

It is believed that there are two general types of wireless servicesthat can be easily supported by the present disclosure: (i) services(denoted “called services” herein) where the wireless subscriberinitiates an activation substantially by placing a telephony call forservice activation (e.g., services similar to E911), and (ii) services(denoted “connection services” herein) that are activated by asubscriber navigating a previously established network (e.g., Internet)connection where the establishment of the network connection providesvirtually no information about what subsequent network services that maybe activated by the subscriber. Such called services may interfacedirectly with an embodiment of the platform of the present disclosure,wherein the embodiment may be for a single wireless carrier or mayprovide such services for multiple carriers. Moreover, for connectionservices, such services may be of two types:

-   -   (1) connection services that make use of the capabilities of an        embodiment of the platform of the present disclosure; e.g., a        “platform aware” application for providing such a connection        service might inspect a network (e.g., Internet) path by which        an activation was received by a subscriber, wherein the        inspection would determine whether there is a platform        embodiment by which the platform aware application can        communicate for receiving appropriate additional information        such as subscriber location, type of mobile device, subscriber        profile attributes (e.g., authorizations for billing a profile        designated entity), and/or for transmitting information to the        platform for billing for and/or logging the activated connection        service (e.g., an electronic yellow pages subsidiary of a        wireless carrier may be activated, via the Internet, by a        merchant for advertising an eminent sale and the expense        incurred is automatically incorporated into the merchant's bill        with the carrier, or, e.g., providing a corporation with an        integrated billing, auditing and employee wireless profile        management system for telecommunications and Internet services        wherein a platform embodiment of the present disclosure acts as        a common interface for both managing employee profiles for        access to network services, and billing the corporation for        employee network accesses to billable network services whose        enabling applications are “platform aware”; and    -   (2) connection services that do not make use of the capabilities        of the platform of the present disclosure. However, even for        these services the platform of the present disclosure may        provide substantial benefits. It is believed that in many (if        not most cases) wherein connection services are accessed via a        platform of the present disclosure, that the entity providing        the connection to the network (e.g., an Internet service        provider) for such connection services will be “platform aware”.        Accordingly, information from a subscriber's profile can be        requested and/or “pushed” to the network connection providing        entity so that, e.g., this entity can prohibit access to certain        network information, or can push corporate specific information        to an employee for incorporation in to the employee's network        connection device (e.g., MS) such as: (i) an updated preferred        vendor list, (ii) a download of a new customer record management        system, and/or (iii) periodically automatically update a        corporate employee address book.

Note, that the functionality of (2) immediately above may be, of course,available to the “platform aware” applications as well.

Thus, it is an aspect of the platform of the present disclosure toprovide for the distribution and use of subscriber or user profileinformation over a plurality of different types of communicationnetworks (e.g., networks having different transmission characteristicssuch as: network bandwidth, the data types that can be effectivelypresented to users, the reliability or quality of service of networktransmissions, and/or the transmission protocols and/or servicesprovided). For example, networks that can classified as different are:different wireless telephony networks (CDMA, TDMA, GSM), wirelinetelephony networks (PSTNs), the Internet or other packet switchednetworks (e.g., networks using WAP). Thus, where there is profileinformation provided for the communication capabilities of individualones of the communication networks and/or the services offered onindividual ones of the communication networks, the platform coordinatesfulfillment of complex service requests that may require the fulfillmentof a plurality of subordinate service requests on potentially differentones of these communication networks according to, e.g., information ina user profile that is accessed by the platform for controlling at leastportions of the fulfillment of the complex service request.

Furthermore, it is a particular aspect of the platform of the presentdisclosure to enable easy implementation of wireless location relatedapplications. For example, embodiments of the platform disclosed hereinhave “plug and play” interfaces so that applications for fulfillingservice requests need only identify to the platform their requirementsand the platform coordinates the activation and routing of results fromother applications operatively attached to the platform.

Examples of wireless location applications enabled by embodiments of thepresent disclosure follow. Wireless applications related to intelligentadvertising (e.g., personalized advertising driven by informationdisclosed by a subscriber or user) may be provided by an embodiment ofthe disclosure, wherein the user's location is used in determining theadvertising provided. Alternatively, wireless applications for providinggames and gaming may also be provided by an embodiment of the platformdisclosed herein. Moreover, for gaming, the platform may supportwireless Internet gaming wherein the geographic location of a wirelessplayer is taken into account for determining any legal restrictions thatmust be obeyed in order to conform with gaming laws where the user islocated. Additional wireless services or applications expeditiouslyenabled by the disclosure include: introductions of wireless users withlikely or stated shared interests (possibly based on locationproximity), labor management and tracking, asset management andtracking, and sightseeing. Other applications are provided in theDetailed Description hereinbelow.

It is an aspect of the present disclosure that for at least somewireless applications, a geographical proximity subsystem or engine isaccessed for determining when the application invoking (or locationmonitored) user or a tracked asset is in proximity to a particularentity (e.g., a location, person, or moving object) that the proximityengine outputs a message to the corresponding invoked application.Conversely, the proximity engine may be used for determining when two ormore entities become further apart than some predetermined distance(e.g., hikers, or children from their home).

It is a further aspect of the present disclosure that a wirelessservices platform as described herein provide such wireless applicationsto wireless users in an “always on” or “always accessible” capabilitymuch like broadcast television wherein the user has access to apredetermined number of wireless services/applications, and the user canselectively activate/deactivate such services/applications dependingupon the user's input. However, it is also an aspect of the presentdisclosure to go beyond the broadcast television paradigm in that: (i) aplurality of such applications can be concurrently active, and (ii) suchapplications can be activated/deactivated according to various criteriasuch as user location, time of day, proximity to/from a particularlocation or entity. Moreover, this “always accessible” capability may bepresented at the user's wireless mobile station via a graphical userinterface such that a proactive intelligent collection of applicationsis available, wherein such applications may function as, e.g.,electronic agents or extensions of a user so that such an agent can,e.g., (i) alert the user of location based circumstances to which theuser would not otherwise be aware, (ii) arrange or facilitatecommunications between users that are in proximity to one another whenit is determined that such communication is likely desired by bothparties wherein these users may have no a priori knowledge of oneanother and/or their common interests. Moreover, embodiments of thepresent disclosure are intended to support “intelligent” wirelesscommunication between a user and a plurality of different wirelessapplications via (at least in one embodiment) substantially the samewireless services platform wherein such applications may be, e.g.,considered as intelligent agents of the user for providing the user withinformation about products, services, people, objects, and/or locationsabout which the user may have an interest but which the user has bothinsufficient knowledge, and an insufficient knowledge to prearrange theobtaining of such information. For example, a user may input userprofile information to the wireless services platform indicating thatthe user should be alerted when any other user that is presumed to bewalking (or stationary), and is nearby (e.g., within 200 feet), and hasa profile indicating that he/she is receptive to contact, and isinterested in purchasing early Asian art. In particular, such alerts maybe very useful if, e.g., a user is a seller of such art and is attendinga well attended art auction or museum displaying Asian art. As anotherexample, if a user is on an airplane, the user may be alerted to otherusers on the airplane wherein it may likely that communication betweenthe two users would be a mutually beneficial based on the (personal orprofessional) profiles of the users.

Moreover, the present disclosure is novel in that it provides a userwith a mobile station interface that allows the user to have a pluralityof such intelligent location sensitive agents/applications activesimultaneously wherein the user is wirelessly notified when any one ormore of these agents/applications detect a condition or circumstancethat may be of interest to the user. Thus, the user may have one or morebusiness related agents/applications active (e.g., for contactingpotential nearby buyers or sellers of products or services), incombination with one or more personal needs related agents/applications(e.g., for meeting a possible nearby compatible mate, or someoneinterested in East European folk dancing, or for purchasing a nearbybicycle below a particular price), in combination with one or moreagents/applications related to nearby entertainment. Moreover suchagents/applications may be explicitly turned on or off by the user atany time (e.g., the user may manually request an immediate one timequery of other users within a specified proximity), as well as the usermay provide criteria for activating and deactivating suchagents/applications according to time schedules, and/or the user'slocation. Thus, the user may request automatic deactivation of personalagents while at work, and activation of such agents when the user isdetected as being away from work. Moreover, the present disclosure mayoffer a plurality generic agents/applications which the user can thencustomize. For example, a first sales representative for a particularcompany may request wireless downloads of current prices for a firstcollection of products or services while a second sales representativemay request wireless downloads of current prices for a second differentcollection of products or services. More generally, the presentdisclosure supports wireless synchronization between a corporateenterprise wide data repository and various corporate subentities suchas subsidiaries, salespersons or other employees, wherein access to thedata repository and wireless data synchronization with a particular viewor subset of the data repository is dependent upon the subentity'saccess permissions as provided by the corporation.

Additionally, the wireless platform disclosed herein may provideservices so that applications/agents can perform data mining of variousnetwork accessible databases to provide verification of data of interestto a user. For example, a user that travels frequently may request thata wireless application perform data mining via, e.g., Internet searchengines for currently available nearby movies, concerts, lecturers, andspecial events whenever the user activates the application. As otherexamples, a user may request data mining be performed to determineinformation such as: the legal description or owner of a particularproperty given the property's address, or the average income ofhouseholds within one mile of the user's location. As other examples, auser may request data mining to be performed for automatically enteringinformation into the user's profile and/or validating information inhis/her profile and/or another user's profile.

Additionally, it is an aspect of the present disclosure that requestsfor location information by a user and/or applications activated by theuser are coordinated so that there is efficient use of wireless locationnetwork capabilities. For example, a first wireless application may beactivated by a user for requesting information related to nearby usersthat have an interest in health products (e.g., the user may be an ownerof a health food store). Additionally, the user may have anotherwireless agent/application active for requesting information aboutnearby individuals that appear to be compatible with the user.Accordingly, the frequency of receiving information on nearby users, andthe sharing of results between the two active agents/applications canprovide better utilization of network resources.

It is another aspect of the present disclosure that when a request foruser information is received such as due to a location based proximityquery, there is a sequence of steps and interactions between therequesting user and the queried user which can lead from substantialanonymity to (if desired by both parties) personal contact in anon-threatening and comfortable manner. In particular, as anintermediate step from substantial anonymity to possibly meetingface-to-face, it is an aspect of the present disclosure to provide aninstant messaging type service between the requesting user and a querieduser wherein the two users can converse without the identity of theother user being automatically provided by the network.

Further features and advantages of the present disclosure are providedby the figures and detailed description accompanying present summary.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates various perspectives of radio propagationopportunities which may be considered in addressing correlation withmobile to base station ranging.

FIG. 2 shows aspects of the two-ray radio propagation model and theeffects of urban clutter.

FIG. 3 provides a typical example of how the statistical power budget iscalculated in design of a Commercial Mobile Radio Service Providernetwork.

FIG. 4 illustrates an overall view of a wireless radio location networkarchitecture, based on advanced intelligent network (AIN) principles.

FIG. 5 is a high level block diagram of an embodiment of the presentdisclosure for locating a mobile station (MS) within a radio coveragearea for an embodiment of the present disclosure.

FIG. 6 is a high level block diagram of the location center 142.

FIG. 7 is a high level block diagram of the hypothesis evaluator for thelocation center.

FIG. 8 is a substantially comprehensive high level block diagramillustrating data and control flows between the components of (and/oraccessed by) the location center/gateway 142, as well the functionalityof these components.

FIGS. 9A and 9B are a high level data structure diagram describing thefields of a location hypothesis object generated by the first ordermodels 1224 of the location center.

FIG. 10 is a graphical illustration of the computation performed by themost likelihood estimator 1344 of the hypothesis evaluator.

FIG. 11 is a high level block diagram of the mobile base station (MBS).

FIG. 12 is a high level state transition diagram describingcomputational states the Mobile Base station enters during operation.

FIG. 13 is a high level diagram illustrating the data structuralorganization of the Mobile Base station capability for autonomouslydetermining a most likely MBS location from a plurality of potentiallyconflicting MBS location estimating sources.

FIG. 14 illustrates the primary components of the signal processingsubsystem.

FIG. 15 illustrates how automatic provisioning of mobile stationinformation from multiple CMRS occurs.

FIG. 16 illustrates another embodiment of the location engine 139,wherein the context adjuster 1326 (denoted in this figure as “locationhypothesis adjuster modules”) includes a module (1436) that is capableof adjusting location hypotheses for reliability, and another module(1440) that is capable of adjusting location hypotheses for accuracy.

FIG. 17 illustrates the primary components of the signal processingsubsystem.

FIG. 18 is a block diagram further illustrating a wireless locationgateway.

FIG. 19 is a block diagram of an electronic networked yellow pages forproviding intelligent advertising services, wherein wireless locationservices may be utilized.

FIG. 20 is a high level block diagram illustrating the wirelessapplication platform of the present disclosure.

FIG. 21 is a more detailed block diagram illustrating the wirelessapplication platform of the present disclosure.

FIG. 22 is a high level flowchart of the operation of the wirelessapplication platform of the present disclosure.

FIGS. 23A and 23B show a flowchart of the steps performed for routing auser along a route that includes a plurality of locations where the usercan access a desired item (product or service) at each of the pluralityof locations.

FIGS. 24A and 24B show a flowchart that is illustrative of the stepsperformed when, e.g., MS user input of preferences and needs isiteratively examined at various user locations for determining thelocation(s) that sufficiently satisfy user specified constraints (e.g.,temporal or situational constraints) so that the user is alerted ornotified of products and/or services that satisfy the user's input. Thesteps 2004 through 2040 are fully disclosed and explained in thesections hereinabove.

DETAILED DESCRIPTION

Detailed Description Introduction

When performing wireless location as described herein, substantialimprovements in radio location can be achieved since CDMA and otheradvanced radio communication infrastructures can be used for enhancingradio location. For example, the capabilities of IS-41 and advancedintelligent network (AIN) already provide a coarse-granularity ofwireless location, as is necessary to, for example, properly direct aterminating call to an MS. Such information, originally intended forcall processing usage, can be re-used in conjunction with the wirelesslocation processing described herein to provide wireless location in thelarge (i.e., to determine which country, state and city a particular MSis located), and wireless location in the small (i.e., which location,plus or minus a few hundred feet a given MS is located).

FIG. 4 is a high level diagram of one embodiment of a wirelessradiolocation architecture for the present disclosure. Accordingly, thisfigure illustrates the interconnections between the components of awireless cellular communication network, such as, a typical PCS networkconfiguration and various components that are specific to the presentdisclosure. In particular, as one skilled in the art will understand, atypical wireless (PCS) network includes:

-   -   (a) a (large) plurality of wireless mobile stations (MSs) 140        for at least one of voice related communication, visual (e.g.,        text such as is provided by a short message service) related        communication, and according to present disclosure, location        related communication. Note that some of the MSs 140 may include        the electronics and corresponding software to detect and process        signals from non-terrestrial transmission stations such as GPS        and/or GLONASS satellites. Moreover, note that such        non-terrestrial transmission stations can also be high attitude        aircraft which, e.g., can hover over a metropolitan area thereby        facilitating wireless communications;    -   (b) a mobile switching center (MSC) 112;    -   (c) a plurality of wireless cell sites in a radio coverage area        120, wherein each cell site includes an infrastructure base        station such as those labeled 122 (or variations thereof such as        122A-122D). In particular, the base stations 122 denote the        standard high traffic, fixed location base stations used for        voice and data communication with a plurality of MSs 140, and,        according to the present disclosure, also used for communication        of information related to locating such MSs 140. Additionally,        note that the base stations labeled 152 are more directly        related to wireless location enablement. For example, as        described in greater detail hereinbelow, the base stations 152        may be low cost, low functionality transponders that are used        primarily in communicating MS location related information to        the location center 142 (via base stations 122 and the MSC 112).        Note that unless stated otherwise, the base stations 152 will be        referred to hereinafter as location base station(s) 152 or        simply LBS(s) 152;    -   (d) a public switched telephone network (PSTN) 124 (which may        include signaling system links 106 having network control        components such as: a service control point (SCP) 104, one or        more signaling transfer points (STPs) 110.

In addition, the present disclosure provides one or more locationcenters/gateways 142. Such gateways may be described at a high level asfollows.

Location Center/Gateway 142 Description

A location center/gateway 142, (also be referred to as a locationcenter/gateway, or simply gateway), in response to a location requestreceived at the location center, can request activation of one or moreof a plurality of wireless location techniques in order to locate an MS140.

Various embodiments are provided herein of the location center/gateway142. In particular, FIG. 18 is block diagram illustrating anotherembodiment of the location center/gateway 142 of the present disclosure.Note that the wireless location gateway activation requests may bedependent upon, e.g.,

-   -   (a) a wireless network with which the MS 140 may be in contact,        such a network may be:        -   (i) a commercial mobile radio network supporting telephony            functionality,        -   (ii) a short messaging service or paging network;        -   (iii) a wireless network of beacons for providing location            related information such as GPS and LORAN C,        -   (iv) wireless carrier independent networks for performing            wireless location such as the wireless location network            provided by Times Three, Suite #220, Franklin Atrium, 3015            5th Avenue N.E,. Calgary, AB T2A 6TB,        -   (v) a wireless broadcasting network for use in activating an            MS 140 of, e.g., a stolen vehicle such as is provided by            LoJack Corporation, 333 Elm Street, Dedham, Mass. 02026,            and/or        -   (vi) a hybrid network including portions of wireless            networks each network providing different types of signal            measurements for performing wireless location);    -   (b) the location signal measurement obtaining capabilities of        the wireless network with which the MS may be in contact. For        example, such a network may only support a network centric        location technique;    -   (c) the functionality of the MS 140 such as: the type(s) of        wireless signals which can be detected and processed by the MS        such as:        -   (i) non-terrestrial signals such as GPS signals,        -   (ii) signals from wireless beaconing/broadcasting systems            such as for LORAN C signals or stolen vehicle broadcast            networks for activating an MS 140 attached to the stolen            vehicle, or        -   (iii) wireless telephony protocols like CDMA, TDMA, and/or            GSM,    -   (d) a likely location of the target MS 140. For example, if the        target MS 140 is likely to be in Japan rather than the United        States, then the location service provider contacted by the        gateway 142 may be different from the location service provider        if the MS is likely to be in the U.S.

Moreover, regarding the plurality of wireless location techniques(embodiments thereof also denoted herein as “location estimators”) forwhich activation may be requested by the gateway, these techniques maybe co-located with the gateway, accessible via a network including: (i)local area networks, and (ii) wide area networks such as a telephony(wired or wireless) network, the Internet or a cable network. Thegateway 142 may supply to one or more of the location estimators,measurements of communications between the MS 140 and one or morenetworks for determining a location of the MS 140. Alternatively,instead of supplying such measurements (locally or remotely, and, via anetwork or otherwise), the gateway 142 may provide, with the locationactivation request, an identification of where the measurements may beobtained (e.g., one or more network addresses). In yet anotheralternative, such a gateway 142 may also send request(s) to thenetwork(s) having such MS communication measurements to forward them toparticular location estimators. Note, that in performing these tasks,the gateway 142 may receive with a location request (or may retrieve inresponse thereto) information regarding the functionality of the targetMS 140, e.g., as discussed above. Accordingly, such information may beused in selecting the location estimator to which an activation requestis provided. Thus, the gateway 142 may be the intermediary betweenlocation requesting applications and the location estimators, therebyproviding a simple, uniform application programming interface (API) forsuch applications substantially independently of the location estimatorsthat are activated to fulfill such location requests. Moreover, thegateway 142 (or embodiments thereof) can substantially ease the burdenon geolocation service providers by providing a substantially uniformmethod for obtaining target MS/network signal data for use in locatingthe target MS. Thus, by interfacing to the gateway 142, a locationservice provider may substantially reduce the number and complexity ofits data exchange interfaces with the wireless networks for obtainingtarget MS/network signal data. Similarly, the networks capturing suchsignal data may also reduce the complexity and number of theirinterfaces for providing such signal data to location service providers.Additionally, note that the gateway may also fulfill location requestswherein the location is for a stationary and/or wireline handset insteadof a mobile station 140. Accordingly, the gateway 142 may request accessto, e.g., phone location information stored in a carrier's database ofpremise provisioning equipment as one skilled in the art willunderstand.

In some embodiments of the gateway 142, it may also facilitate in theproviding of certain location related services in addition to providing,e.g., MS 140 locations. In particular, one or more of the followinglocation related services may be facilitated by the gateway 142 or maybe made operative via the wireless location capabilities of the gateway142. However, note that the following location related services can, ingeneral, be provided without use of a gateway 142, albeit, e.g., in alikely more restricted context wherein not all available wirelesslocation estimating techniques are utilized, and/or by multiplying thenumber of interfaces to geolocation service providers (e.g., distinctwireless location interfaces provided directly to each wireless locationservice provider utilized). Further note that at some of theseapplications are described in greater detail in later sections herein:

-   -   (10.1) Routing instructions for directing a vehicle or person to        get to a desired destination. Note, that there are various forms        of utilizing MS location capabilities to determine an        appropriate route, and related teachings are provided in        copending U.S. patent application titled, “Wireless Location        Using A Plurality of Commercial Network Infrastructures,”        by F. W. LeBlanc, Dupray and Karr filed Jan. 22, 1999 and having        U.S. Pat. No. 6,236,365 issued May 22, 2001 which is fully        incorporated herein by reference, and by the following two        copending U.S. patent applications which are also incorporated        herein by reference: (i) “Location Of A Mobile Station” filed        Nov. 24, 1999 having application Ser. No. 09/194,367 whose        inventors are Dupray and Karr, and    -   (ii) “A Wireless Location System For Calibrating Multiple        Location Estimators” filed Oct. 21, 1998 having application Ser.        No. 09/176,587 whose inventor is Dupray. Additionally, other        routing services may also be provided by the gateway 142 (or by        service providers in cooperation with the gateway). For example,        the gateway 142 may cooperate with an automated speech        recognition interpretation and synthesis unit for providing        substantially automated interactive communication with an MS 140        for providing spoken directions. Note that such directions may        be provided in terms of street names and/or descriptions of the        terrain (e.g., “the glass high rise on the left having pink        tinted glass”).    -   (10.2) Advertising may be directed to an MS 140 according to its        location. In at least some studies it appears that MS 140 users        do not respond well to unsolicited wireless advertisement        whether location based or otherwise. However, in response to        certain user queries for locally available merchandise, certain        advertisements may be viewed in a more friendly light. Thus, by        allowing an MS user to contact, e.g., a wireless advertising        portal by voice or via wireless Internet, and describe certain        merchandise desired (e.g., via interacting with an automated        speech interaction unit) the user may be able to describe and        receive (at his/her MS 140) visual displays of merchandise that        may satisfy such a user's request. For example, an MS user may        provide a spoken request such as: “I need a shirt, who has        specials near here?”.    -   (10.3) Applications that combine routing with safety for        assisting MS users with requests such as “How do I get back to        the hotel safely?”;    -   (10.4) Applications that combine routing with sight seeing        guided tour where routing is interactive and depending on        feedback from users regarding, e.g., user interests;    -   (10.5) Applications using Internet picture capture with real        time voice capture and MS location (e.g., sightseeing, security,        and law enforcement),    -   (10.6) Intelligent transportation (e.g., voice commanded        vehicles)    -   (10.7) Applications that monitor whether or not a person or        object (e.g., a vehicle) is within a predetermined boundary.        Note, that such as application may automatically provide speech        output to the MS user (or other authorized user) when the person        or object is beyond the predetermined boundary;    -   (10.8) Applications that route to an event and automatically        determine parking availability and where to park;    -   (10.9) Traffic/weather condition routing

Further note that various architectures for the location center/locationgateway are within the scope of the disclosure including a distributedarchitecture wherein in addition to the FOMs being possibly remotelyaccessed (e.g., via a communications network such as the Internet), thegateway itself may be distributed throughout one or more communicationnetworks. Thus, a location request received at a first location gatewayportion may be routed to a second location gateway portion (e.g., viathe Internet). Such a distributed gateway may be considered a“meta-gateway” and in fact such gateway portions may be fullyfunctioning gateways in their own right. Thus, such routing therebetweenmay be due to contractual arrangements between the two gateways (eachfulfilling location requests for a different network, wireless carrier,and/or geographical region). For example, for locating a stolen vehicle,it is not uncommon for the stolen vehicle to be transported rapidlybeyond the coverage area of a local or regional wireless vehiclelocating service. Moreover, a given location gateway may providelocation information for only certain areas corresponding, e.g., tocontractual arrangements with the wireless carriers with which thelocation gateway is affiliated. Thus, a first location gateway mayprovide vehicle locations for a first collection of one or more wirelessnetworks, and a second location gateway may provide vehicle locationsfor a second collection of one or more wireless networks. Accordingly,for an MS 140 built into a vehicle which can be detected by one or morewireless networks (or portions thereof) in each of the first and secondcollections, then if the vehicle is stolen, the first gateway may beinitially contacted for determining whether the vehicle can be locatedvia communications with the first collection of one or more wirelessnetworks, and if the vehicle can not be located, the first gateway mayprovide a location request to the second gateway for thereby locatingthe stolen vehicle via wireless communications with one or more wirelessnetworks of the second collection. Furthermore, the first gateway mayprovide location requests for the stolen vehicle to other locationgateways.

The present disclosure provides the following additional components:

-   -   (11.1) one or more mobile base stations 148 (MBS) which are        optional, for physically traveling toward the target MS 140 or        tracking the target MS;    -   (11.2) a plurality of location base stations 152 (LBS) which are        optional, distributed within the radio coverage areas 120, each        LBS 152 having a relatively small MS 140 detection area 154.        Note that such LBSs 152 may also support Internet and/or TCP/IP        transmissions for transmitting visual location related        information (e.g., graphical, or pictorial) related to an MS        location request.

Since location base stations 152 can be located on, e.g., each floor ofa multi-story building, the wireless location technology describedherein can be used to perform location in terms of height as well as bylatitude and longitude.

In operation, an MS 140 may utilize one or more of the wirelesstechnologies, CDMA, TDMA, AMPS, NAMPS or GSM for wireless communicationwith: (a) one or more infrastructure base stations 122, (b) mobile basestation(s) 148, or (c) an LBS 152. Additionally, note that in someembodiments of the disclosure, there may be MS to MS communication.

Referring to FIG. 4 again, additional detail is provided of typical basestation coverage areas, sectorization, and high level components withina radio coverage area 120, including the MSC 112. Three exemplary basestations (BSs) are 122A, 122B and 122C, each of which radiatereferencing signals within their area of coverage 169 to facilitatemobile station (MS) 140 radio frequency connectivity, and various timingand synchronization functions. Note that some base stations may containno sectors 130 (e.g. 122E), thus radiating and receiving signals in a360 degree omnidirectional coverage area pattern, or the base stationmay contain “smart antennas” which have specialized coverage areapatterns. However, the generally most frequent base stations 122 havethree sector 130 coverage area patterns. For example, base station 122Aincludes sectors 130, additionally labeled a, b and c. Accordingly, eachof the sectors 130 radiate and receive signals in an approximate 120degree arc, from an overhead view. As one skilled in the art willunderstand, actual base station coverage areas 169 (stylisticallyrepresented by hexagons about the base stations 122) generally aredesigned to overlap to some extent, thus ensuring seamless coverage in ageographical area. Control electronics within each base station 122 areused to communicate with a mobile stations 140. Information regardingthe coverage area for each sector 130, such as its range, area, and“holes” or areas of no coverage (within the radio coverage area 120),may be known and used by the location center 142 to facilitate locationdetermination. Further, during communication with a mobile station 140,the identification of each base station 122 communicating with the MS140 as well, as any sector identification information, may be known andprovided to the location center 142.

In the case of the base station types 122, 148, and 152 communicatinglocation information, a base station or mobility controller 174 (BSC)controls, processes and provides an interface between originating andterminating telephone calls from/to mobile station (MS) 140, and themobile switch center (MSC) 112. The MSC 122, on-the-other-hand, performsvarious administration functions such as mobile station 140registration, authentication and the relaying of various systemparameters, as one skilled in the art will understand.

The base stations 122 may be coupled by various transport facilities 176such as leased lines, frame relay, T-Carrier links, optical fiber linksor by microwave communication links.

When an MS 140 is powered on and in the idle state, it constantlymonitors the pilot signal transmissions from each of the base stations122 located at nearby cell sites. Since base station/sector coverageareas may often overlap, such overlapping enables an MS 140 to detect,and, in the case of certain wireless technologies, communicatesimultaneously along both the forward and reverse paths, with multiplebase stations 122 and/or sectors 130. In FIG. 4, the constantlyradiating pilot signals from base station sectors 130, such as sectorsa, b and c of BS 122A, are detectable by MSs 140 within the coveragearea 169 for BS 122A. That is, the mobile stations 140 scan for pilotchannels, corresponding to a given base station/sector identifiers(IDs), for determining in which coverage area 169 (i.e., cell) it iscontained. This is performed by comparing signal strengths of pilotsignals transmitted from these particular cell-sites.

The mobile station 140 then initiates a registration request with theMSC 112, via the base station controller 174. The MSC 112 determineswhether or not the mobile station 140 is allowed to proceed with theregistration process (except, e.g., in the case of a 911 call, whereinno registration process is required). Once any required registration iscomplete, calls may be originated from the mobile station 140 or callsor short message service messages can be received from the network. Notethat the MSC 112 communicates as appropriate, with a class 4/5 wirelinetelephony circuit switch or other central offices, connected to the PSTN124 network. Such central offices connect to wireline terminals, such astelephones, or any communication device compatible with a wireline. ThePSTN 124 may also provide connections to long distance networks andother networks.

The MSC 112 may also utilize IS/41 data circuits or trunks connecting tosignal transfer point 110, which in turn connects to a service controlpoint 104, via Signaling System #7 (SS7) signaling links (e.g., trunks)for intelligent call processing, as one skilled in the art willunderstand. In the case of wireless AIN services such links are used forcall routing instructions of calls interacting with the MSC 112 or anyswitch capable of providing service switching point functions, and thepublic switched telephone network (PSTN) 124, with possible terminationback to the wireless network.

Referring still to FIG. 4, the location center/gateway (LC) 142interfaces with the MSC 112 either via dedicated transport facilities178, using, e.g., any number of LAN/WAN technologies, such as Ethernet,fast Ethernet, frame relay, virtual private networks, etc., or via thePSTN 124. The gateway 142 may receive autonomous (e.g., unsolicited)command/response messages regarding, for example: (a) the state of thewireless network of each commercial radio service provider utilizing theLC 142 for wireless location services, (b) MS 140 and BS 122 radiofrequency (RF) measurements, (c) communications with any MBSs 148, and(d) location applications requesting MS locations using the locationcenter/gateway 142. Conversely, the LC 142 may provide data and controlinformation to each of the above components in (a)-(d). Additionally,the LC 142 may provide location information to an MS 140, via a BS 122.Moreover, in the case of the use of a mobile base station (MBS) 148,several communications paths may exist with the LC 142.

The MBS 148 may act as a low cost, partially-functional, moving basestation, and is, in one embodiment, situated in a vehicle (e.g., land,water or aircraft) where an operator may engage in MS 140 searching andtracking activities. In providing these activities using CDMA, the MBS148 provides a forward link pilot channel for a target MS 140, andsubsequently receives unique BS pilot strength measurements from the MS140. The MBS 148 also includes a mobile station 140 for datacommunication with the gateway 142, via a BS 122. In particular, suchdata communication includes telemetering at least the geographicposition (or estimates thereof) of the MBS 148, various RF measurementsrelated to signals received from the target MS 140, and in someembodiments, MBS 148 estimates of the location of the target MS 140. Insome embodiments, the MBS 148 may utilize multiple-beam fixed antennaarray elements and/or a moveable narrow beam antenna, such as amicrowave dish 182. The antennas for such embodiments may have a knownorientation in order to further deduce a radio location of the target MS140 with respect to an estimated current location of the MBS 148. Aswill be described in more detail herein below, the MBS 148 may furthercontain a satellite (e.g., global positioning system (GPS)) receiver (orother receiver for non-terrestrial wireless signals) for determining thelocation of the MBS 148 and/or providing wireless location assistance atarget MS 140, e.g., providing GPS information to the MS to assist theMS in determining its location. Additionally, the MBS 148 may includedistance sensors, dead-reckoning electronics, as well as an on-boardcomputing system and display devices for locating both the MBS 148itself as well as tracking and locating the target MS 140. The computingand display provides a means for communicating the position of thetarget MS 140 on a map display to an operator of the MBS 148. It isimportant to note that in one embodiment, an MBS 148 may determine itslocation substantially independent of the communications network(s) withwhich the MBS communicates.

Each location base station (LBS) 152 is a low cost location device. Insome embodiments, to provide such LBS's cost effectively, each LBS 152only partially or minimally supports the air-interface standards of theone or more wireless technologies used in communicating with both theBSs 122 and the MSs 140. Each LBS 152, when put in service, is placed ata fixed location, such as at a traffic signal, lamp post, etc., whereinthe location of the LBS may be determined as accurately as, for example,the accuracy of the locations of the infrastructure BSs 122. Assumingthe wireless technology, CDMA, is used, each BS 122 uses a time offsetof the pilot PN sequence to identify a forward CDMA pilot channel. Inone embodiment, each LBS 152 emits a unique, time-offset pilot PNsequence channel in accordance with the CDMA standard in the RF spectrumdesignated for BSs 122, such that the channel does not interfere withneighboring BSs 122 cell site channels, and does not interfere withneighboring LBSs 152. Each LBS 152 may also contain multiple wirelessreceivers in order to monitor transmissions from a target MS 140.Additionally, each LBS 152 contains mobile station 140 electronics,thereby allowing the LBS to both be controlled by, e.g., the gateway 142or the wireless carrier(s) for the LBS, and to transmit information to,e.g., the gateway 142 (via, e.g., at least one neighboring BS 122), orto another wireless location service provider such as one providing oneor more FOMs.

As mentioned above, when the location of a particular target MS 140 isdesired, the gateway 142 may request location information about thetarget MS 140 from, for instance, one or more activated LBSs 152 in ageographical area of interest. Accordingly, whenever the target MS 140is in an LBS coverage area, or is suspected of being in the coveragearea, either upon command from the gateway 142 (or other locationservice provider), or in a substantially continuous (or periodic)fashion, the LBS's pilot channel appears to the target MS 140 as apotential neighboring base station channel, and consequently, is placed,for example, in the CDMA neighboring set, or the CDMA remaining set ofthe target MS 140 (as one familiar with the CDMA standards willunderstand).

During the normal CDMA pilot search sequence of the mobile stationinitialization state (in the target MS), the target MS 140 will, ifwithin range of such an activated LBS 152, detect the LBS pilot presenceduring the CDMA pilot channel acquisition substate. Consequently, thetarget MS 140 performs RF measurements on the signal from each detectedLBS 152. Similarly, an activated LBS 152 can perform RF measurements onthe wireless signals from the target MS 140. Accordingly, each LBS 152detecting the target MS 140 may subsequently telemeter back to the LC142 measurement results related to signals from/to the target MS 140.Moreover, upon command, the target MS 140 may telemeter back to thegateway 142 its own measurements of the detected LBSs 152, andconsequently, this new location information, in conjunction withlocation related information received from the BSs 122, can be used tolocate the target MS 140.

It should be noted that an LBS 152 will normally deny hand-off requests,since typically the LBS does not require the added complexity ofhandling voice or traffic bearer channels, although economics and peaktraffic load conditions may dictate preference here. Note that GPStiming information, needed by any CDMA base station, is either achievedvia a the inclusion of a local GPS receiver or via a telemetry processfrom a neighboring conventional BS 122, which contains a GPS receiverand timing information. Since energy requirements are minimal in such anLBS 152, (rechargeable) batteries or solar cells may be used to powerthe LBSs. Further, no expensive terrestrial transport link is typicallyrequired since two-way communication is provided by an included MS 140(or an electronic variation thereof) within each LBS. Thus, LBSs 152 maybe placed in numerous locations, such as:

-   -   (a) in dense urban canyon areas (e.g., where signal reception        may be poor and/or very noisy);    -   (b) in remote areas (e.g., hiking, camping and skiing areas);    -   (c) along highways (e.g., for emergency as well as monitoring        traffic flow), and their rest stations; or    -   (d) in general, wherever more location precision is required        than is obtainable using other wireless infrastructure network        components.        Location Center—Network Elements API Description

A location application programming interface 136 (FIG. 4), denotedL-API, is may be provided between the location center/gateway 142 (LC)and the mobile switch center (MSC) network element type, in order tosend and receive various control, signals and data messages. The L-APImay be implemented using a preferably high-capacity physical layercommunications interface, such as IEEE standard 802.3 (10 baseTEthernet), although other physical layer interfaces could be used, suchas fiber optic ATM, frame relay, etc. At least two forms of L-APIimplementation are possible. In a first case, the signal control anddata messages are provided using the MSC 112 vendor's native operationsmessages inherent in the product offering, without any specialmodifications. In a second case, the L-API includes a full suite ofcommands and messaging content specifically optimized for wirelesslocation purposes, which may require some, although minor development onthe part of an MSC vendor.

Signal Processor Description

Referring to FIG. 17, a signal processing subsystem (labeled 1220 inother figures) may be provided (or accessed) by the gateway 142. Such asignal processing subsystem may: (a) receive control messages and signalmeasurements from one or more wireless service provider networks, and(b) transmit appropriate control messages to such wireless networks viathe location applications programming interface 136 referenced earlier,for wireless location purposes. The signal processing subsystem 1220additionally provides various signal identification, conditioning andpre-processing functions, including buffering, signal typeclassification, signal filtering, message control and routing functionsto the location estimating modules or FOMs.

There can be several combinations of Delay Spread/Signal Strength setsof measurements made available to the signal processing subsystem 1220.In some cases a mobile station 140 (FIG. 1) may be able to detect up tothree or four pilot channels representing three to four base stations,or as few as one pilot channel, depending upon the environment andwireless network configuration. Similarly, possibly more than one BS 122can detect a mobile station 140 transmitter signal, and the fact thatmultiple CMRS' base station equipment commonly will overlap coverageareas.

For each mobile station 140 or BS 122 transmitted signal that isdetected by a receiver group at a base or mobile station, respectively,multiple delayed signals, or “fingers” may be detected (e.g., in CDMA)and tracked resulting from multipath radio propagation conditions from agiven transmitter. In typical spread spectrum diversity CDMA receiverdesign, the “first” finger represents the most direct, or least delayedmultipath signal. Second or possibly third or fourth fingers may also bedetected and tracked, assuming the detecting base station and/or mobilestation 140 contains a sufficient number of data receivers for doing so.The signal processing subsystem may utilize various wireless signalmeasurements of transmissions between a target mobile station 140 and anetwork of base stations 122, 152 and/or 148. Such measurements can beimportant in effectively estimating the location of mobile stations 140in that it is well known that measurements of wireless signalpropagation characteristics, such as signal strength (e.g., RSSI), timedelay, angle of arrival, and any number other measurements, canindividually lead to gross errors in MS 140 location estimates.

Accordingly, one aspect of the present disclosure is directed toutilizing a larger number of wireless signal measurements, and utilizinga plurality of MS 140 estimation techniques to compensate for locationestimation errors generated by some such techniques. For example, due tothe large capital outlay costs associated with providing three or moreoverlapping base station coverage signals in every possible location,most practical digital PCS deployments result in fewer than three basestation pilot channels being reportable in the majority of locationareas, thus resulting in a larger, more amorphous location estimates byterrestrial triangulation systems. Thus, by utilizing wireless signalmeasurements from a variety of sources substantially simultaneouslyand/or “greedily” (i.e., use whatever signal measurements can beobtained from any of the signal sources as they are obtained),additional location enhancements can be obtained. For example, byenhancing a mobile station 140 with electronics for detecting satellitetransmissions (as done with mobile base stations 148 and which also canbe viewed as such an enhanced mobile station 140) additional locationrelated signals maybe obtained from:

-   -   (a) the GPS satellite system,    -   (b) the Global Navigation Satellite System (GLONASS) satellite        system, a Russian counterpart to the U.S. GPS system, and/or    -   (c) the numerous low earth orbit satellite systems (LEOs) and        medium earth orbit satellite systems (MEOs) such as the IRIDIUM        system being developed by Motorola Corp., the GLOBALSTAR system        by Loral and Qualcomm, and the ICO satellite system by ICO        Global Communications.        Thus, by combining even insufficient wireless location        measurements from different wireless communication systems,        accurate location of an MS 140 is possible. For example, by if        only two GPS satellites are detectable, but there is an        additional reliable wireless signal measurement from, e.g., a        terrestrial base station 122, then by triangulating using        wireless signal measurements derived from transmissions from        each of these three sources, a potentially reliable and accurate        MS location can be obtained.

Moreover, the transmissions from the MS 140 used for determining theMS's location need not be transmitted to terrestrial base stations(e.g., 122). It is within the scope of the present disclosure that atarget MS 140 may transmit location related information to satellites aswell. For example, if a target MS 140 detects two GPS satellitetransmissions and is able to subsequently transmit the GPS signalmeasurements (e.g., timing measurements) to an additional satellitecapable of determining additional MS location measurements according tothe signals received, then by performing a triangulation process at thelocation center/gateway 142 (which may be co-located with the additionalsatellite, or at a remote terrestrial site), a potentially reliable andaccurate MS location can be obtained. Accordingly, an embodiment of thepresent disclosure is capable of resolving wireless location ambiguitiesdue to a lack of location related information of one type by utilizingsupplemental location related information of a different type. Note thatby “type” as used here it is intended to be interpreted broadly as,e.g.,

-   -   (a) a data type of location information, and/or    -   (b) communications from a particular commercial wireless system        as opposed to an alternative system, each such system having        distinct groups of known or registered MS users.

Moreover, it can be that different FOMs are provided for at least somewireless location computational models utilizing different types oflocation related information. For example, in certain contexts wirelessnetworks based on different wireless signaling technologies may be usedto locate an MS 140 during the time period of a single emergency callsuch as E911. Moreover, in other contexts it may be possible for thetarget MS 140 to use one or more of a plurality of wirelesscommunication networks, possibly based on different wirelesscommunication technologies, depending on availability the of technologyin the coverage area. In particular, since so called “dual mode” or“tri-mode” mobile stations 140 are available, wherein such mobilestations are capable of wireless communication in a plurality ofwireless communication technologies, such as digital (e.g., CDMA, and/orTDMA) as well as analog or AMP/NAMPS, such mobile stations may utilize afirst (likely a default) wireless communication technology wheneverpossible, but switch to another wireless communication technology when,e.g., coverage of the first wireless technology becomes poor. Moreover,such different technologies are typically provided by different wirelessnetworks (wherein the term “network” is understood to include a networkof communication supporting nodes geographically spaced apart thatprovide a communications infrastructure having access to informationregarding subscribers to the network prior to a request to access thenetwork by the subscribers). Accordingly, an embodiment of the presentdisclosure may include (or access) FOMs for providing mobile stationlocation estimates wherein the target MS 140 communicates with variousnetworks using different wireless communication technologies. Moreover,such FOMs may be activated according to the wireless signal measurementsreceived from various wireless networks and/or wireless technologiessupported by a target MS 140 and to which there is a capability ofcommunicating measurements of such varied wireless signals to theFOM(s). Thus, in one embodiment of the present disclosure, there may bea triangulation (or trilateration) based FOM for each of CDMA, TDMA andAMP/NAMPS which may be singly, serially, or concurrently for obtaining aparticular location of an MS 140 at a particular time (e.g., for an E911call). Thus, when locating a target MS 140, the MS may, if there isoverlapping coverage of two wireless communication technologies and theMS supports communications with both, repeatedly switch back and forthbetween the two thereby providing additional wireless signalmeasurements for use in locating the target MS 140.

In one embodiment of the present disclosure, wherein multiple FOMs maybe activated substantially simultaneously (or alternatively, whereverappropriate input is received that allow particular FOMs to beactivated). Note that at least some of the FOMs may provide “inverse”estimates of where a target MS 140 is not instead of where it is. Suchinverse analysis can be very useful in combination with locationestimates indicating where the target MS is in that the accuracy of aresulting MS location estimate may be substantially decreased in sizewhen such inverse estimates are utilized to rule out areas thatotherwise appear to be likely possibilities for containing the target MS140. Note that one embodiment of a FOM that can provide such reverseanalysis is a location computational model that generates target MSlocation estimates based on archived knowledge of base station coverageareas (such an archive being the result of, e.g., the compilation a RFcoverage database—either via RF coverage area simulations or fieldtests). In particular, such a model may provide target MS locationinverse estimates having a high confidence or likelihood that that thetarget MS 140 is not in an area since either a base station 122 (or 152)can not detect the target MS 140, or the target MS can not detect aparticular base station. Accordingly, the confidences or likelihoods onsuch estimates may be used by diminishing a likelihood that the targetMS is in an area for the estimate, or alternatively the confidence orlikelihood of all areas of interest outside of the estimate canincreased.

Note that in some embodiments of the present disclosure, bothmeasurements of forward wireless signals to a target MS 140, andmeasurements of reverse wireless signals transmitted from the target MSto a base station can be utilized by various FOMs. In some embodiments,the received relative signal strength (RRSS_(BS)) of detected nearbybase station transmitter signals along the forward link to the targetmobile station can be more readily used by the location estimate modules(FOMs) since the transmission power of the base stations 122 typicallychanges little during a communication with a mobile station. However,the relative signal strength (RRSS_(MS)) of target mobile stationtransmissions received by the base stations on the reverse link mayrequire more adjustment prior to location estimate model use, since themobile station transmitter power level changes nearly continuously.

Location Center High Level Functionality

At a very high level the location center/gateway 142 computes (orrequests computation of) location estimates for a wireless mobilestation 140 by performing at least some of the following steps:

(23.0) receiving an MS location request;

(23.1) receiving measurements of signal transmission characteristics ofcommunications communicated between the target MS 140 and one or morewireless infrastructure base stations 122. Note, this step may only beperformed if the gateway provides such measurements to a FOM (e.g, a FOMco-located therewith);

(23.2) filtering the received signal transmission characteristics (by asignal processing subsystem 1220 illustrated in, e.g., FIGS. 5 and 30)as needed so that target MS location data can be generated that isuniform and consistent with location data generated from other targetMSs 140. In particular, such uniformity and consistency is both in termsof data structures and interpretation of signal characteristic valuesprovided by the MS location data, as will be described hereinbelow.Note, this step may also only be performed if the gateway provides suchmeasurements to a FOM. Otherwise, such FOM is likely to perform suchfiltering;

(23.3) inputting the generated target MS location data to one or more MSlocation estimating models (FOMs, labeled collectively as 1224 in FIG.5), so that each such FOM may use the input target MS location data forgenerating a “location hypothesis” providing an estimate of the locationof the target MS 140. Note, this step may also only be performed if thegateway provides such measurements to a FOM;

(23.4) receiving the resulting location hypotheses from the activatedFOMs, and providing the generated location hypotheses to an hypothesisevaluation module (denoted the hypothesis evaluator 1228 in FIG. 5) for:

(a) (optionally) adjusting the target MS location estimates of thegenerated location hypotheses and/or adjusting confidence values of thelocation hypotheses, wherein for each location hypothesis, itsconfidence value indicates the confidence or likelihood that the targetMS is located in the location estimate of the location hypothesis.Moreover, note that such adjusting uses archival information related tothe accuracy and/or reliability of previously generated locationhypotheses;

(b) (optionally) evaluating the location hypotheses according to variousheuristics related to, for example, the radio coverage area 120 terrain,the laws of physics, characteristics of likely movement of the target MS140; and

(c) (necessarily) determining a most likely location area for the targetMS 140, wherein the measurement of confidence associated with each inputMS location area estimate may be used for determining a “most likelylocation area”; and

(23.5) outputting a most likely target MS location estimate to one ormore applications 146 (FIG. 5) requesting an estimate of the location ofthe target MS 140.

Location Hypothesis Data Representation

In order to describe how the steps (23.1) through (23.5) are performedin the sections below, some introductory remarks related to the datadenoted above as location hypotheses will be helpful. Additionally, itwill also be helpful to provide introductory remarks related tohistorical location data and the data base management programsassociated therewith.

For each target MS location estimate generated and utilized by anembodiment of the present disclosure, the location estimate may beprovided in a data structure (or object class) denoted as a “locationhypothesis” (illustrated in Table LH-1). Brief descriptions of the datafields for a location hypothesis is provided in the Table LH-1. TABLELH-1 FOM_ID First order model ID (providing this Location Hypothesis);note, since it is possible for location hypotheses to be generated byother than the FOMs 1224, in general, this field identifies the modulethat generated this location hypothesis. MS_ID The identification of thetarget MS 140 to this location hypothesis applies. pt_est The mostlikely location point estimate of the target MS 140. valid_pt Booleanindicating the validity of “pt_est”. area_est Location Area Estimate ofthe target MS 140 provided by the FOM. This area estimate will be usedwhenever “image_area” below is NULL. valid_area Boolean indicating thevalidity of “area_est” (one of “pt_est” and “area_est” must be valid).adjust Boolean (true if adjustments to the fields of this locationhypothesis are to be performed in the Context adjuster Module).pt_covering Reference to a substantially minimal area (e.g., mesh cell)covering of “pt_est”. Note, since this MS 140 may be substantially on acell boundary, this covering may, in some cases, include more than onecell. image_area Reference to a substantially minimal area (e.g., meshcell) covering of “pt_covering” (see detailed description of thefunction, “confidence_adjuster”). Note that if this field is not NULL,then this is the target MS location estimate used by the location center142 instead of “area_est”. extrapolation_area Reference to (if non-NULL)an extrapolated MS target estimate area provided by the locationextrapolator submodule 1432 of the hypothesis analyzer 1332. That is,this field, if non-NULL, is an extrapolation of the “image_area” fieldif it exists, otherwise this field is an extrapolation of the “area_est”field. Note other extrapolation fields may also be provided depending onthe embodiment of the present disclosure, such as an extrapolation ofthe “pt_covering”. Confidence In one embodiment, this is a probabilityindicating a likelihood that the target MS 140 is in (or out) of aparticular area. If “image_area” exists, then this is a measure of thelikelihood that the target MS 140 is within the area represented by“image_area”, or if “image_area” has not been computed (e.g., “adjust”is FALSE), then “area_est” must be valid and this is a measure of thelikelihood that the target MS 140 is within the area represented by“area_est”. Other embodiments, are also within the scope of the presentdisclosure that are not probabilities; e.g., translations and/orexpansions of the [0, 1] probability range as one skilled in the artwill understand. Original_Timestamp Date and time that the locationsignature cluster (defined hereinbelow) for this location hypothesis wasreceived by the signal processing subsystem 1220. Active_TimestampRun-time field providing the time to which this location hypothesis hashad its MS location estimate(s) extrapolated (in the locationextrapolator 1432 of the hypothesis analyzer 1332). Note that this fieldis initialized with the value from the “Original_Timestamp” field.Processing Tags and For indicating particular types of environmentalenvironmental classifications not readily determined by thecategorizations “Original_Timestamp” field (e.g., weather, traffic), andrestrictions on location hypothesis processing. loc_sig_cluster Providesaccess to the collection of location signature signal characteristicsderived from communications between the target MS 140 and the basestation(s) detected by this MS (discussed in detail hereinbelow); inparticular, the location data accessed here is provided to the firstorder models by the signal processing subsystem 1220; i.e., access tothe “loc sigs” (received at “timestamp” regarding the location of thetarget MS) descriptor Original descriptor (from the First order modelindicating why/how the Location Area Estimate and Confidence Value weredetermined).

As can be seen in the Table LH-1, each location hypothesis datastructure includes at least one measurement, denoted hereinafter as aconfidence value (or simply confidence), that is a measurement of theperceived likelihood that an MS location estimate in the locationhypothesis is an accurate location estimate of the target MS 140. Since,in some embodiments of the disclosure, such confidence values are animportant aspect, much of the description and use of such confidencevalues are described below; however, a brief description is providedhere.

In one embodiment, each confidence value is a probability indicative ofa likeliness that the target MS 140 resides within an geographic arearepresented by the hypothesis to which the confidence value applies.Accordingly, each such confidence value is in the range [0, 1].Moreover, for clarity of discussion, it is assumed that unless statedotherwise that the probabilistic definition provided here is to be usedwhen confidence values are discussed.

Note, however, other definitions of confidence values are within thescope of the present disclosure that may be more general thanprobabilities, and/or that have different ranges other than [0, 1]. Forexample, one such alternative is that each such confidence value is inthe range −1.0 to 1.0, wherein the larger the value, the greater theperceived likelihood that the target MS 140 is in (or at) acorresponding MS location estimate of the location hypothesis to whichthe confidence value applies. As an aside, note that a locationhypothesis may have more than one MS location estimate (as will bediscussed in detail below) and the confidence value will typically onlycorrespond or apply to one of the MS location estimates in the locationhypothesis. Further, values for the confidence value field may beinterpreted as: (a) −1.0 means that the target MS 140 is NOT in such acorresponding MS area estimate of the location hypothesis area, (b) 0means that it is unknown as to the likelihood of whether the MS 140 inthe corresponding MS area estimate, and (c) +1.0 means that the MS 140is perceived to positively be in the corresponding MS area estimate.

Additionally, in utilizing location hypotheses in, for example, thelocation evaluator 1228 as in (23.4) above, it is important to keep inmind that for confidences, cf₁ and cf₂, if cf₁<=cf₂, then for a locationhypotheses H₁ and H₂ having cf₁ and cf₂, respectively, the target MS 140is expected to more likely reside in a target MS estimate of H₂ than atarget MS estimate of H₁. Moreover, if an area, A, is such that it isincluded in a plurality of location hypothesis target MS estimates, thena confidence score, CS_(A), can be assigned to A, wherein the confidencescore for such an area is a function of the confidences for all thelocation hypotheses whose (most pertinent) target MS location estimatescontain A. That is, in order to determine a most likely target MSlocation area estimate for outputting from the location center/gateway142, a confidence score is determined for areas within the locationcenter/gateway service area.

Coverage Area Types and Their Determination

The notion of “area type” as related to wireless signal transmissioncharacteristics has been used in many investigations of radio signaltransmission characteristics. Some investigators, when investigatingsuch signal characteristics of areas have used somewhat naive areaclassifications such as urban, suburban, rural, etc. However, it isdesirable for the purposes of the present disclosure to have a moreoperational definition of area types that is more closely associatedwith wireless signal transmission behaviors.

To describe embodiments of the an area type scheme that may be used inthe present disclosure, some introductory remarks are first provided.Note that the wireless signal transmission behavior for an area dependson at least the following criteria:

-   -   (23.8.1) substantially invariant terrain characteristics (both        natural and man-made) of the area; e.g., mountains, buildings,        lakes, highways, bridges, building density;    -   (23.8.2) time varying environmental characteristics (both        natural and man-made) of the area; e.g., foliage, traffic,        weather, special events such as baseball games;    -   (23.8.3) wireless communication components or infrastructure in        the area; e.g., the arrangement and signal communication        characteristics of the base stations 122 in the area (e.g., base        station antenna downtilt). Further, the antenna characteristics        at the base stations 122 may be important criteria.

Accordingly, a description of wireless signal characteristics fordetermining area types could potentially include a characterization ofwireless signaling attributes as they relate to each of the abovecriteria. Thus, an area type might be: hilly, treed, suburban, having nobuildings above 50 feet, with base stations spaced apart by two miles.However, a categorization of area types is desired that is both moreclosely tied to the wireless signaling characteristics of the area, andis capable of being computed substantially automatically and repeatedlyover time. Moreover, for a wireless location system, the primarywireless signaling characteristics for categorizing areas into at leastminimally similar area types are: thermal noise and, more importantly,multipath characteristics (e.g., multipath fade and time delay).

Focusing for the moment on the multipath characteristics, it is believedthat (23.8.1) and (23.8.3) immediately above are, in general, moreimportant criteria for accurately locating an MS 140 than (23.8.2). Thatis, regarding (23.8.1), multipath tends to increase as the density ofnearby vertical area changes increases. For example, multipath isparticularly problematic where there is a high density of high risebuildings and/or where there are closely spaced geographic undulations.In both cases, the amount of change in vertical area per unit of area ina horizontal plane (for some horizontal reference plane) may be high.Regarding (23.8.3), the greater the density of base stations 122, theless problematic multipath may become in locating an MS 140. Moreover,the arrangement of the base stations 122 in the radio coverage area 120in FIG. 4 may affect the amount and severity of multipath.

Accordingly, it would be desirable to have a method and system forstraightforwardly determining area type classifications related tomultipath, and in particular, multipath due to (23.8.1) and (23.8.3).The present disclosure provides such a determination by utilizing anovel notion of area type, hereinafter denoted “transmission area type”(or, “area type” when both a generic area type classification scheme andthe transmission area type discussed hereinafter are intended) forclassifying “similar” areas, wherein each transmission area type classor category is intended to describe an area having at least minimallysimilar wireless signal transmission characteristics. That is, the noveltransmission area type scheme of the present disclosure is based on: (a)the terrain area classifications; e.g., the terrain of an areasurrounding a target MS 140, (b) the configuration of base stations 122in the radio coverage area 120, and (c) characterizations of thewireless signal transmission paths between a target MS 140 location andthe base stations 122.

In one embodiment of a method and system for determining such(transmission) area type approximations, a partition (denotedhereinafter as P₀) is imposed upon the radio coverage area 120 forpartitioning for radio coverage area into subareas, wherein each subareais an estimate of an area having included MS 140 locations that arelikely to have is at least a minimal amount of similarity in theirwireless signaling characteristics. To obtain the partition P₀ of theradio coverage area 120, the following steps are performed:

-   -   (23.8.4.1) Partition the radio coverage area 120 into subareas,        wherein in each subarea is: (a) connected, (b) the subarea is        not too oblong, e.g., the variations in the lengths of chords        sectioning the subarea through the centroid of the subarea are        below a predetermined threshold, (c) the size of the subarea is        below a predetermined value, and (d) for most locations (e.g.,        within a first or second deviation) within the subarea whose        wireless signaling characteristics have been verified, it is        likely (e.g., within a first or second deviation) that an MS 140        at one of these locations will detect (forward transmission        path) and/or will be detected (reverse transmission path) by a        same collection of base stations 122. For example, in a CDMA        context, a first such collection may be (for the forward        transmission path) the active set of base stations 122, or, the        union of the active and candidate sets, or, the union of the        active, candidate and/or remaining sets of base stations 122        detected by “most” MSs 140 in. Additionally (or alternatively),        a second such collection may be the base stations 122 that are        expected to detect MSs 140 at locations within the subarea. Of        course, the union or intersection of the first and second        collections is also within the scope of the present disclosure        for partitioning the radio coverage area 120 according to (d)        above. It is worth noting that it is believed that base station        122 power levels will be substantially constant. However, even        if this is not the case, one or more collections for (d) above        may be determined empirically and/or by computationally        simulating the power output of each base station 122 at a        predetermined level. Moreover, it is also worth mentioning that        this step is relatively straightforward to implement using the        data stored in the location signature data base 1320 (i.e., the        verified location signature clusters discussed in detail        hereinbelow). Denote the resulting partition here as P₁.    -   (23.8.4.2) Partition the radio coverage area 120 into subareas,        wherein each subarea appears to have substantially homogeneous        terrain characteristics. Note, this may be performed        periodically substantially automatically by scanning radio        coverage area images obtained from aerial or satellite imaging.        For example, EarthWatch Inc. of Longmont, Colo. can provide        geographic with 3 meter resolution from satellite imaging data.        Denote the resulting partition here as P₂.    -   (23.8.4.3) Overlay both of the above partitions, P₁ and P₂ of        the radio coverage area 120 to obtain new subareas that are        intersections of the subareas from each of the above partitions.        This new partition is P₀ (i.e., P₀=P₁ intersect P₂), and the        subareas of it are denoted as “P₀ subareas”.

Now assuming P₀ has been obtained, the subareas of P₀ are provided witha first classification or categorization as follows:

-   -   (23.8.4.4) Determine an area type categorization scheme for the        subareas of P₁. For example, a subarea, A, of P₁, may be        categorized or labeled according to the number of base stations        122 in each of the collections used in (23.8.4.1)(d) above for        determining subareas of P₁. Thus, in one such categorization        scheme, each category may correspond to a single number x (such        as 3), wherein for a subarea, A, of this category, there is a        group of x (e.g., three) base stations 122 that are expected to        be detected by a most target MSs 140 in the area A. Other        embodiments are also possible, such as a categorization scheme        wherein each category may correspond to a triple: of numbers        such as (5, 2, 1), wherein for a subarea A of this category,        there is a common group of 5 base stations 122 with two-way        signal detection expected with most locations (e.g., within a        first or second deviation) within A, there are 2 base stations        that are expected to be detected by a target MS 140 in A but        these base stations can not detect the target MS, and there is        one base station 122 that is expected to be able to detect a        target MS in A but not be detected.    -   (23.8.4.5) Determine an area type categorization scheme for the        subareas of P₂. Note that the subareas of P₂ may be categorized        according to their similarities. In one embodiment, such        categories may be somewhat similar to the naive area types        mentioned above (e.g., dense urban, urban, suburban, rural,        mountain, etc.). However, it is also an aspect of the present        disclosure that more precise categorizations may be used, such        as a category for all areas having between 20,000 and 30,000        square feet of vertical area change per 11,000 square feet of        horizontal area and also having a high traffic volume (such a        category likely corresponding to a “moderately dense urban” area        type).    -   (23.8.4.6) Categorize subareas of P₀ with a categorization        scheme denoted the “P₀ categorization,” wherein for each P₀        subarea, A, a “P₀ area type” is determined for A according to        the following substep(s):        -   (a) Categorize A by the two categories from (23.8.4.4) and            (23.8.5) with which it is identified. Thus, A is categorized            (in a corresponding P₀ area type) both according to its            terrain and the base station infrastructure configuration in            the radio coverage area 120.    -   (23.8.4.7) For each P₀ subarea, A, of P₀ perform the following        step(s):        -   (a) Determine a centroid, C(A), for A;        -   (b) Determine an approximation to a wireless transmission            path between C(A) and each base station 122 of a            predetermined group of base stations expected to be in (one            and/or two-way) signal communication with most target MS 140            locations in A. For example, one such approximation is a            straight line between C(A) and each of the base stations 122            in the group. However, other such approximations are within            the scope of the present disclosure, such as, a generally            triangular shaped area as the transmission path, wherein a            first vertex of this area is at the corresponding base            station for the transmission path, and the sides of the            generally triangular shaped defining the first vertex have a            smallest angle between them that allows A to be completely            between these sides.        -   (c) For each base station 122, BS_(i), in the group            mentioned in (b) above, create an empty list, BS_(i)-list,            and put on this list at least the P₀ area types for the            “significant” P₀ subareas crossed by the transmission path            between C(A) and BS_(i). Note that “significant” P₀ subareas            may be defined as, for example, the P₀ subareas through            which at least a minimal length of the transmission path            traverses. Alternatively, such “significant” P₀ subareas may            be defined as those P₀ subareas that additionally are know            or expected to generate substantial multipath.        -   (d) Assign as the transmission area type for A as the            collection of BS_(i)-lists. Thus, any other P₀ subarea            having the same (or substantially similar) collection of            lists of P₀ area types will be viewed as having            approximately the same radio transmission characteristics.

Note that other transmission signal characteristics may be incorporatedinto the transmission area types. For example, thermal noisecharacteristics may be included by providing a third radio coverage area120 partition, P₃, in addition to the partitions of P₁ and P₂ generatedin (23.8.4.1) and (23.8.4.2) respectively. Moreover, the time varyingcharacteristics of (23.8.2) may be incorporated in the transmission areatype frame work by generating multiple versions of the transmission areatypes such that the transmission area type for a given subarea of P₀ maychange depending on the combination of time varying environmentalcharacteristics to be considered in the transmission area types. Forinstance, to account for seasonality, four versions of the partitions P₁and P₂ may be generated, one for each of the seasons, and subsequentlygenerate a (potentially) different partition P₀ for each season.Further, the type and/or characteristics of base station 122 antennasmay also be included in an embodiment of the transmission area type.

Other embodiments of area types are also within the scope of the presentdisclosure. As mentioned above, each of the first order models 1224 havedefault confidence values associated therewith, and these confidencevalues may be probabilities. More precisely, such probability confidencevalues can be determined as follows. Assume there is a partition of thecoverage area into subareas, each subarea being denoted a “partitionarea.” For each partition area, activate each first order model 1224with historical location data in the Location Signature Data Base 1320(FIG. 6), wherein the historical location data has been obtained fromcorresponding known mobile station locations in the partition area. Foreach first order model, determine a probability of the first order modelgenerating a location hypothesis whose location estimate contains thecorresponding known mobile station location. To accomplish this, assumethe coverage area is partitioned into partition areas A, wherein eachpartition area A is specified as the collection of coverage arealocations such that for each location, the detected wirelesstransmissions between the network base stations and a target mobilestation at the location can be straightforwardly equated with otherlocations of area A. For example, one such partition, P₀, can be definedwherein each partition area A is specified in terms of three sets ofbase station identifiers, namely, (a) the base station identifiers ofthe base stations that can be both detected at each location of A andcan detect a target mobile station at each location, (b) the identifiersfor base stations that can detect a target mobile station at eachlocation of A, but can not be detected by the target mobile station, and(c) the identifiers for base stations that can be detected by a targetmobile station at each location of A, but these base stations can notdetect the target mobile station. That is, two locations, l₁ and l₂. areidentified as being in A if and only if the three sets of (a), (b), and(c) for l₁ are, respectively, identical to the three sets of (a), (b),and (c) for l₂.

Accordingly, assuming the partition P₀ is used, a description can begiven as to how probabilities may be assigned as the confidence valuesof location hypotheses generated by the first order models 1224. Foreach partition area A, a first order model 1224 is supplied withwireless measurements of archived location data in the LocationSignature Data Base associated with corresponding verified mobilestation locations. Thus, a probability can be determined as to howlikely the first order model is to generate a location hypothesis havinga location estimate containing the corresponding verified mobile stationlocation. Accordingly, a table of partition area probabilities can bedetermined for each first order model 1224. Thus, when a locationhypothesis is generated and identified as belonging to one of thepartition areas, the corresponding probability for that partition areamay be assigned as the confidence value for the location hypothesis. Theadvantages to using actual probabilities here is that, as will bediscussed below, the most likelihood estimator 1344 can compute astraightforward probability for each distinct intersection of themultiple location hypotheses generated by the multiple first ordermodels, such that each such probability indicates a likelihood that thetarget mobile station is in the corresponding intersection.

Location Information Data Bases And Data

Location Data Bases Introduction

It is an aspect of the present disclosure that MS location processingperformed by the location center/gateway 142 should become increasinglybetter at locating a target MS 140 both by (a) building an increasinglymore detailed model of the signal characteristics of locations in theservice area for the present disclosure, and also (b) by providingcapabilities for the location center processing to adapt toenvironmental changes.

One way these aspects of the present disclosure are realized is byproviding one or more data base management systems and data bases for:

(a) storing and associating wireless MS signal characteristics withknown locations of MSs 140 used in providing the signal characteristics.Such stored associations may not only provide an increasingly bettermodel of the signal characteristics of the geography of the servicearea, but also provide an increasingly better model of more changeablesignal characteristic affecting environmental factors such as weather,seasons, and/or traffic patterns;

(b) adaptively updating the signal characteristic data stored so that itreflects changes in the environment of the service area such as, forexample, a new high rise building or a new highway.

Referring again to FIG. 5 of the collective representation of these databases is the location information data bases 1232. Included among thesedata bases is a data base for providing training and/or calibration datato one or more trainable/calibratable FOMs 1224, as well as an archivaldata base for archiving historical MS location information related tothe performance of the FOMs. These data bases will be discussed asnecessary hereinbelow. However, a further brief introduction to thearchival data base is provided here. Accordingly, the term, “locationsignature data base” is used hereinafter to denote the archival database and/or data base management system depending on the context of thediscussion. The location signature data base (shown in, for example,FIG. 6 and labeled 1320) is a repository for wireless signalcharacteristic data derived from wireless signal communications betweenan MS 140 and one or more base stations 122, wherein the correspondinglocation of the MS 140 is known and also stored in the locationsignature data base 1320. More particularly, the location signature database 1320 associates each such known MS location with the wirelesssignal characteristic data derived from wireless signal communicationsbetween the MS 140 and one or more base stations 122 at this MSlocation. Accordingly, it is an aspect of the present disclosure toutilize such historical MS signal location data for enhancing thecorrectness and/or confidence of certain location hypotheses as will bedescribed in detail in other sections below.

Data Representations for the Location Signature Data Base

In one embodiment, there are four fundamental entity types (or objectclasses in an object oriented programming paradigm) utilized in thelocation signature data base 1320. Briefly, these data entities aredescribed in the items (24.1) through (24.4) that follow:

(24.1) (verified) location signatures: Each such (verified) locationsignature describes the wireless signal characteristic measurementsbetween a given base station (e.g., BS 122 or LBS 152) and an MS 140 ata (verified or known) location associated with the (verified) locationsignature. That is, a verified location signature corresponds to alocation whose coordinates such as latitude-longitude coordinates areknown, while simply a location signature may have a known or unknownlocation corresponding with it. Note that the term (verified) locationsignature is also denoted by the abbreviation, “(verified) loc sig”hereinbelow;

(24.2) (verified) location signature clusters: Each such (verified)location signature cluster includes a collection of (verified) locationsignatures corresponding to all the location signatures between a targetMS 140 at a (possibly verified) presumed substantially stationarylocation and each BS (e.g., 122 or 152) from which the target MS 140 candetect the BS's pilot channel regardless of the classification of the BSin the target MS (i.e., for CDMA, regardless of whether a BS is in theMS's active, candidate or remaining base station sets, as one skilled inthe art will understand). Note that for simplicity here, it is presumedthat each location signature cluster has a single fixed primary basestation to which the target MS 140 synchronizes or obtains its timing;

(24.3) “composite location objects (or entities)”: Each such entity is amore general entity than the verified location signature cluster. Anobject of this type is a collection of (verified) location signaturesthat are associated with the same MS 140 at substantially the samelocation at the same time and each such loc sig is associated with adifferent base station. However, there is no requirement that a loc sigfrom each BS 122 for which the MS 140 can detect the BS's pilot channelis included in the “composite location object (or entity)”; and

(24.4) MS location estimation data that includes MS location estimatesoutput by one or more MS location estimating first order models 1224,such MS location estimate data is described in detail hereinbelow.

It is important to note that a loc sig is, in one embodiment, aninstance of the data structure containing the signal characteristicmeasurements output by the signal filtering and normalizing subsystemalso denoted as the signal processing subsystem 1220 describing thesignals between: (i) a specific base station 122 (BS) and (ii) a mobilestation 140 (MS), wherein the BS's location is known and the MS'slocation is assumed to be substantially constant (during a 2-5 secondinterval in one embodiment of the present disclosure), duringcommunication with the MS 140 for obtaining a single instance of loc sigdata, although the MS location may or may not be known. Further, fornotational purposes, the BS 122 and the MS 140 for a loc sig hereinafterwill be denoted the “BS associated with the loc sig”, and the “MSassociated with the loc sig” respectively. Moreover, the location of theMS 140 at the time the loc sig data is obtained will be denoted the“location associated with the loc sig” (this location possibly beingunknown).

Note that additional description of this aspect of the presentdisclosure can be found in one of the following two copending U.S.patent applications which are incorporated herein by reference: (a)“Location Of A Mobile Station” filed Nov. 24, 1999 having applicationSer. No. 09/194,367 whose inventors are D. J. Dupray and C. L. Karr, and(b) “A Wireless Location System For Calibrating Multiple LocationEstimators” filed Oct. 21, 1998 having application Ser. No. 09/176,587whose inventor is D. J. Dupray, wherein these copending patentapplications may have essential material for the present specification.In particular, these copending patent applications may have essentialmaterial relating to the location signature data base 1320.

Location Center Architecture

Overview of Location Center/Gateway Functional Components

FIG. 5 presents a high level diagram of an embodiment of the locationcenter/gateway 142 and the location engine 139 in the context of theinfrastructure for the entire location system of the present disclosure.

It is important to note that the architecture for the locationcenter/gateway 142 and the location engine 139 provided by the presentdisclosure is designed for extensibility and flexibility so that MS 140location accuracy and reliability may be enhanced as further locationdata become available and as enhanced MS location techniques becomeavailable. In addressing the design goals of extensibility andflexibility, the high level architecture for generating and processingMS location estimates may be considered as divided into the followinghigh level functional groups described hereinbelow.

Low Level Wireless Signal Processing Subsystem for Receiving andConditioning Wireless Signal Measurements

A first functional group of location engine 139 modules is forperforming signal processing and filtering of MS location signal datareceived from a conventional wireless (e.g., CDMA) infrastructure, asdiscussed in the steps (23.1) and (23.2) above. This group is denotedthe signal processing subsystem 1220 herein. One embodiment of such asubsystem is described in the U.S. copending patent application titled,“Wireless Location Using A Plurality of Commercial NetworkInfrastructures,” by F. W. LeBlanc, Dupray and Karr filed Jan. 22, 1999and having U.S. Pat. No. 6,236,365. Note that this U.S. patent isincorporated herein entirely by reference

Initial Location Estimators: First Order Models

A second functional group of modules at least accessible by the locationengine 139 are the FOM 1224 for generating various target MS 140location initial estimates, as described in step (23.3). A briefdescription of some types of first order models is provided immediatelybelow. Note that FIG. 8 illustrates another, more detail view of anembodiment of the location center/gateway 142 for the presentdisclosure. In particular, this figure illustrates some of the FOMs 1224at least accessible (but not necessarily co-located with the otherlocation center/gateway modules shown in this figure), and additionallyillustrates the primary communications with other modules of thegateway. However, it is important to note that the present disclosure isnot limited to the FOMs 1224 shown and discussed herein. That is, it isa primary aspect of the present disclosure to easily incorporate FOMsusing other signal processing and/or computational location estimatingtechniques than those presented herein. Further, note that each FOM typemay have a plurality of its MS location estimating models (at least)accessible by the gateway 142.

For example, (as will be described in further detail below), one suchtype of model or FOM 1224 (hereinafter models of this type are referredto as “terrestrial communication station offset (TCSO) models” or“terrestrial communication station offset (TCSO) first order models”, or“terrestrial communication station offset (TCSO) FOMs”) may be based ona range, offset, and/or distance computation such as on a base stationsignal reception angle determination between the target MS 140 from eachof one or more base stations. Basically, such TCSO models 1224 determinea location estimate of the target MS 140 by determining an offset fromeach of one or more base stations 122, possibly in a particulardirection from each (some of) the base stations, so that, e.g., anintersection of each area locus defined by the base station offsets mayprovide an estimate of the location of the target MS. TCSO FOMs 1224 maycompute such offsets based on, e.g.:

-   -   (a) signal timing measurements between the target mobile station        140 and one or more base stations 122; e.g., timing measurements        such as time difference of arrival (TDOA), or time of arrival        (TOA). Note that both forward and reverse signal path timing        measurements may be utilized;    -   (b) signal strength measurements (e.g., relative to power        control settings of the MS 140 and/or one or more BS 122);        and/or    -   (c) signal angle of arrival measurements, or ranges thereof, at        one or more base stations 122 (such angles and/or angular ranges        provided by, e.g., base station antenna sectors having angular        ranges of 120° or 60°, or, so called “SMART antennas” with        variable angular transmission ranges of 2° to 120°).        Accordingly, a terrestrial communication station offset (TCSO)        model may utilize, e.g., triangulation or trilateration to        compute a location hypothesis having either an area location or        a point location for an estimate of the target MS 140.        Additionally, in some embodiments location hypothesis may        include an estimated error.

Another type of FOM 1224 is a statistically based first order model1224, wherein a statistical technique, such as regression techniques(e.g., least squares, partial least squares, principle decomposition),or e.g., Bollenger Bands (e.g., for computing minimum and maximum basestation offsets). In general, models of this type output locationhypotheses determined by performing one or more statistical techniquesor comparisons between the verified location signatures in locationsignature data base 1320, and the wireless signal measurements from atarget MS. Models of this type are also referred to hereinafter as a“stochastic signal (first order) model” or a “stochastic FOM” or a“statistical model.” Of course, statistically based FOMs may be a hybridcombination with another type of FOM such as a TCSO FOM.

Still another type of FOM 1224 is an adaptive learning model, such as anartificial neural net or a genetic algorithm, wherein the FOM may betrained to recognize or associate each of a plurality of locations witha corresponding set of signal characteristics for communications betweenthe target MS 140 (at the location) and the base stations 122. Moreover,typically such a FOM is expected to accurately interpolate/extrapolatetarget MS 140 location estimates from a set of signal characteristicsfrom an unknown target MS 140 location. Models of this type are alsoreferred to hereinafter variously as “artificial neural net models” or“neural net models” or “trainable models” or “learning models.” Notethat a related type of FOM 1224 is based on pattern recognition. TheseFOMs can recognize patterns in the signal characteristics ofcommunications between the target MS 140 (at the location) and the basestations 122 and thereby estimate a location area of the target MS.However, such FOMs may not be trainable.

Yet another type of FOM 1224 can be based on a collection of dispersedlow power, low cost fixed location wireless transceivers (also denoted“location base stations 152” hereinabove) that are provided fordetecting a target MS 140 in areas where, e.g., there is insufficientbase station 122 infrastructure coverage for providing a desired levelof MS 140 location accuracy. For example, it may uneconomical to providehigh traffic wireless voice coverage of a typical wireless base station122 in a nature preserve or at a fair ground that is only populated afew days out of the year. However, if such low cost location basestations 152 can be directed to activate and deactivate via thedirection of a FOM 1224 of the present type, then these location basestations can be used to both location a target MS 140 and also provideindications of where the target MS is not. For example, if there arelocation base stations 152 populating an area where the target MS 140 ispresumed to be, then by activating these location base stations 152,evidence may be obtained as to whether or not the target MS is actuallyin the area; e.g., if the target MS 140 is detected by a location basestation 152, then a corresponding location hypothesis having a locationestimate corresponding to the coverage area of the location base stationmay have a very high confidence value. Alternatively, if the target MS140 is not detected by a location base station 152, then a correspondinglocation hypothesis having a location estimate corresponding to thecoverage area of the location base station may have a very lowconfidence value. Models of this type are referred to hereinafter as“location base station models.”

Yet another type of FOM 1224 can be based on input from a mobile basestation 148, wherein location hypotheses may be generated from target MS140 location data received from the mobile base station 148.

Still other types of FOM 1224 can be based on various techniques forrecognizing wireless signal measurement patterns and associatingparticular patterns with locations in the coverage area 120. Forexample, artificial neural networks or other learning models can used asthe basis for various FOMs.

Note that the FOM types mentioned here as well as other FOM types arediscussed in detail hereinbelow. Moreover, it is important to keep inmind that in one embodiment of the present disclosure, the substantiallysimultaneous use or activation of a potentially large number of suchfirst order models 1224, may be able to enhance both the reliability oflocation estimates and the accuracy of such estimates. Additionally,note that in some embodiments of the present disclosure, the first ordermodels 1224 can be activated when appropriate signal measurements areobtained. For example, a TDOA FOM may be activated when only a singlesignal time delay measurement is obtained from some plurality of basestation 122. However, if, for instance, additional time delay values areobtained (and assuming such additional values are necessary), then oneor more wireless signal pattern matching FOM may also be activated inconjunction with the TDOA FOM. Additionally, a FOM using satellitesignals (e.g., GPS) to perform a triangulation may be activated wheneverappropriate measurements are received regardless of whether additionalFOMs are capable of being substantially simultaneously activated or not.Accordingly, since such satellite signal FOMs are generally moreaccurate, output from such a FOM may dominate any other previous orsimultaneous estimates unless there is evidence to the contrary.

Moreover, the present disclosure provides a framework for incorporatingMS location estimators to be subsequently provided as new FOMs in astraightforward manner. For example, a FOM 1224 based on wireless signaltime delay measurements from a distributed antenna system for wirelesscommunication may be incorporated into embodiments of the presentdisclosure for thereby locating a target MS 140 in an enclosed areaserviced by the distributed antenna system. Accordingly, by using such adistributed antenna FOM, embodiments of the present disclosure maydetermine the floor of a multi-story building from which a target MS istransmitting. Thus, MSs 140 can be located in three dimensions usingsuch a distributed antenna FOM. Additionally, FOMs for detecting certainregistration changes within, for example, a public switched telephonenetwork can also be used for locating a target MS 140. For example, forsome MSs 140 there may be an associated or dedicated device for eachsuch MS that allows the MS to function as a cordless phone to a linebased telephone network when the device detects that the MS is withinsignaling range. In one use of such a device (also denoted herein as a“home base station”), the device registers with a home location registerof the public switched telephone network when there is a status changesuch as from not detecting the corresponding MS to detecting the MS, orvisa versa, as one skilled in the art will understand. Accordingly, byproviding a FOM that accesses the MS status in the home locationregister, the location engine 139 can determine whether the MS is withinsignaling range of the home base station or not, and generate locationhypotheses accordingly. Moreover, other FOMs based on, for example,chaos theory and/or fractal theory are also within the scope of thepresent disclosure.

It is important to note the following aspects of the present disclosurerelating to FOMs 1224:

-   (28.1) Each such first order model 1224 may be relatively easily    incorporated into and/or removed from embodiments of the present    disclosure. For example, assuming that the signal processing    subsystem 1220 provides uniform input to the FOMs, and there is a    uniform FOM output interface (e.g., API), it is believed that a    large majority (if not substantially all) viable MS location    estimation strategies may be accommodated. Thus, it is    straightforward to add or delete such FOMs 1224.-   (28.2) First order models 1224 may be relatively simple and still    provide significant MS 140 locating functionality and    predictability. For example, much of what is believed to be common    or generic MS location processing has been coalesced into, for    example: a location hypothesis evaluation subsystem, denoted the    hypotheses evaluator 1228 and described immediately below. Thus,    embodiments of the present disclosure can be modular and extensible    such that, for example, (and importantly) different first order    models 1224 may be utilized depending on the signal transmission    characteristics of the geographic region serviced by an embodiment    of the present disclosure. Thus, a simple configuration of the    present disclosure may have (or access) a small number of FOMs 1224    for a simple wireless signal environment (e.g., flat terrain, no    urban canyons and low population density). Alternatively, for    complex wireless signal environments such as in cities like San    Francisco, Tokyo or New York, a large number of FOMs 1224 may be    simultaneously utilized for generating MS location hypotheses.    An Introduction to an Evaluator for Location Hypotheses: Hypothesis    Evaluator

A third functional group of location engine 139 modules evaluateslocation hypotheses output by the first order models 1224 and therebyprovides a “most likely” target MS location estimate. The modules forthis functional group are collectively denoted the hypothesis evaluator1228.

Hypothesis Evaluator

A primary purpose of the hypothesis evaluator 1228 is to mitigateconflicts and ambiguities related to location hypotheses output by thefirst order models 1224 and thereby output a “most likely” estimate ofan MS for which there is a request for it to be located. In providingthis capability, there are various related embodiments of the hypothesisevaluator that are within the scope of the present disclosure. Sinceeach location hypothesis includes both an MS location area estimate anda corresponding confidence value indicating a perceived confidence orlikelihood of the target MS being within the corresponding location areaestimate, there is a monotonic relationship between MS location areaestimates and confidence values. That is, by increasing an MS locationarea estimate, the corresponding confidence value may also be increased(in an extreme case, the location area estimate could be the entirecoverage area 120 and thus the confidence value may likely correspond tothe highest level of certainty; i.e., +1.0). Accordingly, given a targetMS location area estimate (of a location hypothesis), an adjustment toits accuracy may be performed by adjusting the MS location area estimateand/or the corresponding confidence value. Thus, if the confidence valueis, for example, excessively low then the area estimate may be increasedas a technique for increasing the confidence value. Alternatively, ifthe estimated area is excessively large, and there is flexibility in thecorresponding confidence value, then the estimated area may be decreasedand the confidence value also decreased. Thus, if at some point in theprocessing of a location hypothesis, if the location hypothesis isjudged to be more (less) accurate than initially determined, then (i)the confidence value of the location hypothesis may be increased(decreased), and/or (ii) the MS location area estimate can be decreased(increased). Moreover, note that when the confidence values areprobabilities, such adjustments are may require the reactivation of oneor more FOMs 1224 with requests to generate location hypotheses havinglocation estimates of different sizes. Alternatively, adjuster modules1436 and/or 1440 (FIG. 16 discussed hereinbelow) may be invoked forgenerating location hypotheses having area estimates of different sizes.Moreover, the confidence value on such an adjusted location hypothesis(actually a new location hypothesis corresponding to the originallygenerated hypothesis) may also be a probability in that combinations ofFOMs 1224 and adjuster modules 1436 and 1440 can also be calibrated forthereby yielding probabilities as confidence values to the resultinglocation hypotheses.

In a first class of embodiments (typically wherein the confidence valuesare not maintained as probabilities), the hypothesis evaluator 1228evaluates location hypotheses and adjusts or modifies only theirconfidence values for MS location area estimates and subsequently usesthese MS location estimates with the adjusted confidence values fordetermining a “most likely” MS location estimate for outputting.Alternatively, in a second class of embodiments for the hypothesisevaluator 1228 (also typically wherein the confidence values are notmaintained as probabilities), MS location area estimates can be adjustedwhile confidence values remain substantially fixed. However, in onepreferred embodiment of the present embodiment, both location hypothesisarea estimates and confidence values are modified.

The hypothesis evaluator 1228 may perform any or most of the followingtasks depending on the embodiment of the hypothesis evaluator. That is,

(30.1) it may enhance the accuracy of an initial location hypothesisgenerated by an FOM by using the initial location hypothesis as,essentially, a query or index into the location signature data base 1320for obtaining one or more corresponding enhanced location hypotheses,wherein the enhanced location hypotheses have both an adjusted target MSlocation area estimates and an adjusted confidences based on pastperformance of the FOM in the location service surrounding the target MSlocation estimate of the initial location hypothesis;

Additionally, for embodiments of the hypothesis evaluator 1228 whereinthe confidence values for location hypotheses are not maintained asprobabilities, the following additional tasks (30.2) through (30.7) maybe performed:

(30.2) the hypothesis evaluator 1228 may utilize environmentalinformation to improve and reconcile location hypotheses supplied by thefirst order models 1224. A basic premise in this context is that theaccuracy of the individual first order models may be affected by variousenvironmental factors such as, for example, the season of the year, thetime of day, the weather conditions, the presence of buildings, basestation failures, etc.;

(30.3) the hypothesis evaluator 1228 may determine how well theassociated signal characteristics used for locating a target MS comparewith particular verified loc sigs stored in the location signature database 1320 (see the location signature data base section for furtherdiscussion regarding this aspect of the disclosure). That is, for agiven location hypothesis, verified loc sigs (which were previouslyobtained from one or more verified locations of one or more MS's) areretrieved for an area corresponding to the location area estimate of thelocation hypothesis, and the signal characteristics of these verifiedloc sigs are compared with the signal characteristics used to generatethe location hypothesis for determining their similarities andsubsequently an adjustment to the confidence of the location hypothesis(and/or the size of the location area estimate);

(30.4) the hypothesis evaluator 1228 may determine if (or how well) suchlocation hypotheses are consistent with well known physical constraintssuch as the laws of physics. For example, if the difference between aprevious (most likely) location estimate of a target MS and a locationestimate by a current location hypothesis requires the MS to:

-   -   (a1) move at an unreasonably high rate of speed (e.g., 200 mph),        or    -   (b1) move at an unreasonably high rate of speed for an area        (e.g., 80 mph in a corn patch), or    -   (c1) make unreasonably sharp velocity changes (e.g., from 60 mph        in one direction to 60 mph in the opposite direction in 4 sec),        then the confidence in the current Location Hypothesis is likely        to be reduced.

Alternatively, if for example, the difference between a previouslocation estimate of a target MS and a current location hypothesisindicates that the MS is:

-   -   (a2) moving at an appropriate velocity for the area being        traversed, or    -   (b2) moving along an established path (e.g., a freeway), then        the confidence in the current location hypothesis may be        increased.        (30.5) the hypothesis evaluator 1228 may determine consistencies        and inconsistencies between location hypotheses obtained from        different first order models. For example, if two such location        hypotheses, for substantially the same timestamp, have estimated        location areas where the target MS is likely to be and these        areas substantially overlap, then the confidence in both such        location hypotheses may be increased. Additionally, note that a        velocity of an MS may be determined (via deltas of successive        location hypotheses from one or more first order models) even        when there is low confidence in the location estimates for the        MS, since such deltas may, in some cases, be more reliable than        the actual target MS location estimates;        (30.6) the hypothesis evaluator 1228 determines new (more        accurate) location hypotheses from other location hypotheses.        For example, this module may generate new hypotheses from        currently active ones by decomposing a location hypothesis        having a target MS location estimate intersecting two radically        different wireless signaling area types. Additionally, this        module may generate location hypotheses indicating areas of poor        reception; and        (30.7) the hypothesis evaluator 1228 determines and outputs a        most likely location hypothesis for a target MS.

Note that additional description of the hypothesis evaluator 1228 can befound in one of the following two copending U.S. patent applicationswhich are incorporated herein by reference: (a) “Location Of A MobileStation” filed Nov. 24, 1999 having application Ser. No. 09/194,367whose inventors are D. J. Dupray and C. L. Karr, and (b) “A WirelessLocation System For Calibrating Multiple Location Estimators” filed Oct.21, 1998 having application Ser. No. 09/176,587 whose inventor is D. J.Dupray, wherein these copending patent applications may have essentialmaterial for the present specification. In particular, these copendingpatent applications may have essential material relating to theirdescriptions of the hypothesis evaluator.

Context Adjuster Introduction.

The context adjuster (alternatively denoted “location adjuster modules)1326 module enhances both the comparability and predictability of thelocation hypotheses output by the first order models 1224. In oneembodiment (typically where confidence values of location hypotheses arenot maintained as probabilities), this module modifies locationhypotheses received from the FOMs 1224 so that the resulting locationhypotheses output by the context adjuster 1326 may be further processeduniformly and substantially without concern as to differences inaccuracy between the first order models from which location hypothesesoriginate. Further, embodiments of the context adjuster may determinethose factors that are perceived to impact the perceived accuracy (e.g.,confidence) of the location hypotheses:. For instance, environmentalcharacteristics may be taken into account here, such as time of day,season, month, weather, geographical area categorizations (e.g., denseurban, urban, suburban, rural, mountain, etc.), area subcategorizations(e.g., heavily treed, hilly, high traffic area, etc.).

In FIG. 16, two such adjuster modules are shown, namely, an adjuster forenhancing reliability 1436 and an adjuster for enhancing accuracy 1440.Both of these adjusters perform their location hypothesis adjustments inthe manner described above. The difference between these two adjustermodules 1436 and 1440 is primarily the size of the localized area“nearby” the newly generated location estimate. In particular, since itis believed that the larger (smaller) the localized nearby area is, themore likely (less likely) the corresponding adjusted image is to containthe target mobile station location, the adjuster for enhancingreliability 1436 may determine its localized areas “nearby” a newlygenerated location estimate as, for example, having a 40% largerdiameter (alternatively, area) than the location area estimate generatedby a first order model 1224. Alternatively, the adjuster for enhancingaccuracy 1444 may determine its localized areas “nearby” a newlygenerated location estimate as, for example, having a 30% smallerdiameter (alternatively, area) than the location area estimate generatedby a first order model 1224. Thus, each newly generated locationhypothesis can potentially be used to derive at least two additionaladjusted location hypotheses with some of these adjusted locationhypotheses being more reliable and some being more accurate than thelocation hypotheses generated directly from the first order models 1224.

Note that additional description of context adjuster aspects of thepresent disclosure can be found in the following two copending U.S.patent applications which are incorporated herein by reference: (a)“Location Of A Mobile Station” filed Nov. 24, 1999 having applicationSer. No. 09/194,367 whose inventors are D. J. Dupray and C. L. Karr, and(b) “A Wireless Location System For Calibrating Multiple LocationEstimators” filed Oct. 21, 1998 having application Ser. No. 09/176,587whose inventor is D. J. Dupray, wherein these copending patentapplications may have essential material for the present specification.In particular, these copending patent applications may have essentialmaterial relating to the context adjuster 1326.

MS Status Repository Introduction

The MS status repository 1338 is a run-time storage manager for storinglocation hypotheses from previous activations of the location engine 139(as well as for storing the output “most likely” target MS locationestimate(s)) so that a target MS 140 may be tracked using target MSlocation hypotheses from previous location engine 139 activations todetermine, for example, a movement of the target MS 140 betweenevaluations of the target MS location.

Location Hypothesis Analyzer Introduction.

The location hypothesis analyzer 1332, may adjust confidence values ofthe location hypotheses, according to:

-   -   (a) heuristics and/or statistical methods related to how well        the signal characteristics for the generated target MS location        hypothesis matches with previously obtained signal        characteristics for verified MS locations.    -   (b) heuristics related to how consistent the location hypothesis        is with physical laws, and/or highly probable reasonableness        conditions relating to the location of the target MS and its        movement characteristics. For example, such heuristics may        utilize knowledge of the geographical terrain in which the MS is        estimated to be, and/or, for instance, the MS velocity,        acceleration or extrapolation of an MS position, velocity, or        acceleration.    -   (c) generation of additional location hypotheses whose MS        locations are consistent with, for example, previous estimated        locations for the target MS.

Note that additional description of this aspect of the presentdisclosure can be found in one of the following copending U.S. patentapplication which is incorporated herein by reference: “Location Of AMobile Station” filed Nov. 24, 1999 having application Ser. No.09/194,367 whose inventors are D. J. Dupray and C. L. Karr.

Most Likelihood Estimator

The most likelihood estimator 1344 is a module for determining a “mostlikely” location estimate for a target MS being located by the locationengine 139. The most likelihood estimator 1344 receives a collection ofactive or relevant location hypotheses from the hypothesis analyzer 1332and uses these location hypotheses to determine one or more most likelyestimates for the target MS 140.

There are various embodiments of the most likelihood estimator 1344 thatmay be utilized with embodiments of the present disclosure. One suchembodiment will now be described. At a high level, an area of interestis first determined which contains the target MS 140 whose location isdesired. This can be straightforwardly determined by identifying thebase stations 122 that can be detected by the target MS 140 and/or thebase stations 140 that can detect the target MS. Subsequently, assumingthat this area of interest has been previously partitioned into “cells”(e.g., small rectangular areas of, for example, 50 to 200 feet per side)and that the resulting location hypotheses for estimating the locationof the target MS 140 each have a likelihood probability associatedtherewith, then for each such location hypothesis, a probability (moregenerally confidence value) is capable of being assigned to each cellintersecting and/or included in the associated target MS locationestimate. In particular, for each location hypothesis, a portion of theprobability value, P, for the associated location estimate, A, can beassigned to each cell, C, intersecting the estimate. One simple way toperform this is to divide P by the number of cells C, and increment, foreach cell C, a corresponding probability indicative of the target MS 140being in C with the result from the division. One skilled in the artwill readily recognize numerous other ways of incrementing such cellprobabilities, including: providing a Gaussian or other probabilisticdistribution of probability values according to, e.g., the distance ofthe cell from the centroid of the location estimate. Accordingly,assuming all such probability increments have been assigned to all suchcells C from all location hypotheses generated for locating the targetMS 140, then the following is one embodiment of a program fordetermining one or more most likely locations of the target MS.Desired_rel

get the desired reliability for the resulting location estimate;Max_size

get the desired maximum extent for the resulting location estimate;Binned_cells

sort the cells of the area of interest by their probabilities into binswhere each     successive bin includes those cells whose confidencevalues are within a smaller     (non-overlapping) range from that of anypreceding bin. Further, assume there     are, e.g., 100 bins B_(I)wherein B₁ has cells with confidences within the range [0,     0.1], andB_(I) has cells with confidences within the range [(i − 1) * 0.01, i *0.01]. Result

nil; Curr_rel

0; /* current likelihood of target MS 140 being in the area representedby “Result” */ Done

FALSE; Repeat   Cell_bin

get first (next) bin of cells from Binned_cells;   While (there arecells in Cell_bin) do     Curr_cell

get a next cell from Cell_bin that is closest to the centroid of        “Result”;     Result

Result + Curr_cell;     /* now determine a new reliability valuecorresponding to adding “Curr_cell” to      the most likely locationestimate being built in “Result” */     Curr_rel

Curr_rel + confidence_of_MS_in(Curr_cell);     If (Curr_rel >Desired_rel) then       Done

TRUE; Until Done; /* reliability that the target MS is in “Result” issufficient */ Curr_size

current maximum geographic extent (i.e., dimension) of the arearepresented by       “Result”; If (Curr_size <= Max_size) thenoutput(Result); Else Determine whether “Result” has one or more outlyingcells that can be replaced by other cells  closer to the centroid of“Result” and still have a reliability >= “Desired_rel”;  If (there arereplaceable outlier cells) then   replace them in Result andoutput(Result);  Else output(Result);

Note that numerous similar embodiments of the above program maybe used,as one skilled in the art will understand. For instance, instead of“building” Result as provided in the above program, Result can be“whittled” from the area of interest. Accordingly, Result would beinitialized to the entire area of interest, and cells would be selectedfor removal from Result. Additionally, note that the above programdetermines a fast approximation to the optimal most likely areacontaining the target MS 140 having at least a particular desiredconfidence. However, a similar program may be readily provided where amost likely area having less than a desired extent or dimension isoutput; e.g., such a program would could be used to provide an answer tothe question: “What city block is the target MS most likely in?”

Additionally, note that a center of gravity type of computation forobtaining the most likely location estimate of the target MS 140 may beused as described in U.S. Pat. No. 5,293,642 ('642 patent) filed Dec.19, 1990 having an issue data of Mar. 8, 1994 with inventor Lo which isincorporated by reference herein.

Still referring to the hypothesis evaluator 1228, it is important tonote that not all the above mentioned modules are required in allembodiments of the present disclosure. In particular, the hypothesisanalyzer 1332 may be unnecessary. Accordingly, in such an embodiment,the enhanced location hypotheses output by the context adjuster 1326 areprovided directly to the most likelihood estimator 1344.

Control and Output Gating Modules

A fourth functional group of location engine 139 modules is the controland output gating modules which includes the location center controlsubsystem 1350, and the output gateway 1356. The location controlsubsystem 1350 provides the highest level of control and monitoring ofthe data processing performed by the location center 142. In particular,this subsystem performs the following functions:

-   -   (a) controls and monitors location estimating processing for        each target MS 140. Note that this includes high level exception        or error handling functions;    -   (b) receives and routes external information as necessary. For        instance, this subsystem may receive (via, e.g., the public        telephone switching network and Internet 468) such environmental        information as increased signal noise in a particular service        area due to increase traffic, a change in weather conditions, a        base station 122 (or other infrastructure provisioning), change        in operation status (e.g., operational to inactive);    -   (c) receives and directs location processing requests from other        location centers 142 (via, e.g., the Internet);    -   (d) performs accounting and billing procedures such as billing        according to MS location accuracy and the frequency with which        an MS is located;    -   (e) interacts with location center operators by, for example,        receiving operator commands and providing output indicative of        processing resources being utilized and malfunctions;    -   (f) provides access to output requirements for various        applications requesting location estimates. For example, an        Internet location request from a trucking company in Los Angeles        to a location center 142 in Denver may only want to know if a        particular truck or driver is within the Denver area.        Alternatively, a local medical rescue unit is likely to request        a precise a location estimate as possible.

Note that in FIG. 6, (a)-(d) above are, at least at a high level,performed by utilizing the operator interface 1374.

Referring now to the output gateway 1356, this module routes target MS140 location estimates to the appropriate location application(s). Forinstance, upon receiving a location estimate from the most likelihoodestimator 1344, the output gateway 1356 may determine that the locationestimate is for an automobile being tracked by the police and thereforemust be provided must be provided according to the particular protocol.

System Tuning and Adaptation: The Adaptation Engine

A fifth functional group of location engine 139 modules provides theability to enhance the MS locating reliability and/or accuracy ofembodiments of the present disclosure by providing it with thecapability to adapt to particular operating configurations, operatingconditions and wireless signaling environments without performingintensive manual analysis of the performance of various embodiments ofthe location engine 139. That is, this functional group automaticallyenhances the performance of the location engine for locating MSs 140within a particular coverage area 120 using at least one wirelessnetwork infrastructure therein. More precisely, this functional groupallows embodiments of the present disclosure to adapt by tuning oroptimizing certain system parameters according to location engine 139location estimate accuracy and reliability.

There are a number location engine 139 system parameters whose valuesaffect location estimation, and it is an aspect of the presentdisclosure that the MS location processing performed should becomeincreasingly better at locating a target MS 140 not only throughbuilding an increasingly more detailed model of the signalcharacteristics of location in the coverage area 120 such as discussedabove regarding the location signature data base 1320, but also byproviding automated capabilities for the location center processing toadapt by adjusting or “tuning” the values of such location center systemparameters.

Accordingly, embodiments of the present disclosure may include a module,denoted herein as an “adaptation engine” 1382, that performs anoptimization procedure on the location center 142 system parameterseither periodically or concurrently with the operation of the locationcenter in estimating MS locations. That is, the adaptation engine 1382directs the modifications of the system parameters so that the locationengine 139 increases in overall accuracy in locating target MSs 140. Inone embodiment, the adaptation engine 1382 includes an embodiment of agenetic algorithm as the mechanism for modifying the system parameters.Genetic algorithms are basically search algorithms based on themechanics of natural genetics.

Note that additional description of this aspect of the presentdisclosure can be found in one of the following two copending U.S.patent applications which are incorporated herein by reference: (a)“Location Of A Mobile Station” filed Nov. 24, 1999 having applicationSer. No. 09/194,367 whose inventors are D. J. Dupray and C. L. Karr, and(b) “A Wireless Location System For Calibrating Multiple LocationEstimators” filed Oct. 21, 1998 having application Ser. No. 09/176,587whose inventor is D. J. Dupray, wherein these copending patentapplications may have essential material for the present specification.In particular, these copending patent applications may have essentialmaterial relating to the use of genetic algorithm implementations foradaptively tuning system parameters of a particular embodiment of thepresent disclosure.

Implementations of First Order Models

Further descriptions of various first order models 1224 are provided inthis section. However, it is important to note that these are merelyrepresentative embodiments of location estimators that are within thescope of the present disclosure. In particular, two or more of thewireless location technologies described hereinbelow may be combined tocreated additional First Order Models. For example, varioustriangulation techniques between a target MS 140 and the base stationinfrastructure (e.g., time difference of arrival (TDOA) or time ofarrival (TOA)), may be combined with an angle of arrival (AOA)technique. For instance, if a single direct line of sight anglemeasurement and a single direct line of sight distance measurementdetermined by, e.g., TDOA or TOA can effectively location the target MS140. In such cases, the resulting First Order Models may be morecomplex. However, location hypotheses may generated from such modelswhere individually the triangulation techniques and the AOA techniqueswould be unable to generate effective location estimates.

Terrestrial Communication Station Offset (TCSO) First Order Models(e.g., TOA/TDOA/AOA)

As discussed in the Location Center Architecture Overview section hereinabove, TCSO models determine a presumed direction and/or distance (moregenerally, an offset) that a target MS 140 is from one or more basestations 122. In some embodiments of TCSO models, the target MS locationestimate(s) generated are obtained using radio signal analysistechniques that are quite general and therefore are not capable oftaking into account the peculiarities of the topography of a particularradio coverage area. For example, substantially all radio signalanalysis techniques using conventional procedures (or formulas) arebased on “signal characteristic measurements” such as:

-   -   (a) signal timing measurements (e.g., TOA and TDOA), and/or    -   (b) signal strength measurements.        Furthermore, such signal analysis techniques are likely        predicated on certain very general assumptions that can not        fully account for signal attenuation and multipath due to a        particular radio coverage area topography.

Taking CDMA or TDMA base station network as an example, each basestation (BS) 122 is required to emit a constant signal-strength pilotchannel pseudo-noise (PN) sequence on the forward link channelidentified uniquely in the network by a pilot sequence offset andfrequency assignment. It is possible to use the pilot channels of theactive, candidate, neighboring and remaining sets, maintained in thetarget MS, for obtaining signal characteristic measurements (e.g., TOAand/or TDOA measurements) between the target MS 140 and the basestations in one or more of these sets.

Based on such signal characteristic measurements and the speed of signalpropagation, signal characteristic ranges or range differences relatedto the location of the target MS 140 can be calculated. Using TOA and/orTDOA ranges as exemplary, these ranges can then be input to either theradius-radius multilateration or the time difference multilaterationalgorithms along with the known positions of the corresponding basestations 122 to thereby obtain one or more location estimates of thetarget MS 140. For example, if there are, four base stations 122 in theactive set, the target MS 140 may cooperate with each of the basestations in this set to provide signal arrival time measurements.Accordingly, each of the resulting four sets of three of these basestations 122 may be used to provide an estimate of the target MS 140 asone skilled in the art will understand. Thus, potentially (assuming themeasurements for each set of three base stations yields a feasiblelocation solution) there are four estimates for the location of thetarget MS 140. Further, since such measurements and BS 122 positions canbe sent either to the network or the target MS 140, location can bedetermined in either entity.

Since many of the signal measurements utilized by embodiments of TCSOmodels are subject to signal attenuation and multipath due to aparticular area topography. Many of the sets of base stations from whichtarget MS location estimates are desired may result in either nolocation estimate, or an inaccurate location estimate.

Accordingly, some embodiments of TCSO FOMs may attempt to mitigate suchambiguity or inaccuracies by, e.g., identifying discrepancies (orconsistencies) between arrival time measurements and other measurements(e.g., signal strength), these discrepancies (or consistencies) may beused to filter out at least those signal measurements and/or generatedlocation estimates that appear less accurate. In particular, suchidentifying and filtering may be performed by, for example, an expertsystem residing in the TCSO FOM.

Another approach for enhancing certain location techniques such as TDOAor angle or arrival (AOA) is that of super resolution as disclosed inU.S. Pat. No. 5,890,068 filed on Oct. 3, 1996 having an issue date ofMar. 30, 1999 with inventors Fattouche et. al. which is incorporated byreference herein. In particular, the following portions of the '068patent are particularly important: the Summary section, the DetailedDescription portion regarding FIGS. 12-17, and the section titled“Description Of The Preferred Embodiments Of The Invention.” Inparticular the '068 patent describes a technique for estimating a timeof arrival (TOA) of a received signal relative to a time reference ateach one of a plurality of wireless signal monitoring stations using aninverse transform whose resolution is greater than Rayleigh resolution.

Another approach, regardless of the FOM utilized, for mitigating suchambiguity or conflicting MS location estimates is particularly novel inthat each of the target MS location estimates is used to generate alocation hypothesis regardless of its apparent accuracy. Accordingly,these location hypotheses are input to an embodiment of the contextadjuster 1326. In particular, in one context adjuster 1326 embodimenteach location hypothesis is adjusted according to past performance ofits generating FOM 1224 in an area of the initial location estimate ofthe location hypothesis (the area, e.g., determined as a function ofdistance from this initial location estimate), this alternativeembodiment adjusts each of the location hypotheses generated by a firstorder model according to a past performance of the model as applied tosignal characteristic measurements from the same set of base stations122 as were used in generating the location hypothesis. That is, insteadof only using only an identification of the first order model (i.e., itsFOM ID) to, for example, retrieve archived location estimates generatedby the model in an area of the location hypothesis' estimate (whendetermining the model's past performance), the retrieval retrieves thearchived location estimates that are, in addition, derived from thesignal characteristics measurement obtained from the same collection ofbase stations 122 as was used in generating the location hypothesis.Thus, the adjustment performed by this embodiment of the contextadjuster 1326 adjusts according to the past performance of the distancemodel and the collection of base stations 122 used.

Note in one embodiment, such adjustments can also be implemented using aprecomputed vector location error gradient field. Thus, each of thelocation error vectors (as determined by past performance for the FOM)of the gradient field has its starting location at a location previouslygenerated by the FOM, and its vector head at a corresponding verifiedlocation where the target MS 140 actually was. Accordingly, for alocation hypothesis of an unknown location, this embodiment determinesor selects the location error vectors having starting locations within asmall area (e.g., possibly of a predetermined size, but alternatively,dependent on the density of the location error vector starting locationsnearby to the location hypothesis) of the location hypothesis.Additionally, the determination or selection may also be based upon asimilarity of signal characteristics also obtained from the target MS140 being located with signal characteristics corresponding to thestarting locations of location error vectors of the gradient field. Forexample, such sign characteristics may be, e.g., time delay/signalstrength multipath characteristics.

Angle of Arrival First Order Model

Various mobile station location estimating models can be based on theangle of arrival (AOA) of wireless signals transmitted from a target MS140 to the base station infrastructure as one skilled in the art willunderstand. Such AOA models (sometimes also referred to as direction ofarrival or DOA models) typically require precise angular measurements ofthe wireless signals, and accordingly utilize specialized antennas atthe base stations 122. The determined signal transmission angles aresubject to multipath aberrations. Therefore, AOA is most effective whenthere is an unimpeded line-of-sight simultaneous transmission betweenthe target MS 140 and at least two base stations 122.

TCSO (Grubeck) FOM with Increased Accuracy Via Multiple MS Transmissions

Another TCSO first order model 1224, denoted the Grubeck model (FOM)herein, is disclosed in U.S. Pat. No. 6,009,334 filed Nov. 26, 1997 andissued Dec. 28, 1999 having Grubeck, Fischer, and Lundqvist asinventors, this patent being fully incorporated herein by reference. TheGrubeck model includes a location estimator for determining moreaccurately the distance between a wireless receiver at (RX), e.g., aCMRS fixed location communication station (such as a BS 122) and atarget MS 140, wherein wireless signals are repeatedly transmitted fromthe target MS 140 and may be subject to multipath. An embodiment of theGrubeck model may be applied to TOA, TDOA, and/or AOA wirelessmeasurements. For the TOA case, the following steps are performed:

-   -   (a) transmitting “M” samples s_(i) I<=1<=M of the same wireless        signal from, e.g., the target MS 140 to the RX. Preferably M is        on the order of 50 to 100 (e.g., 70) wireless signal bursts,        wherein each such burst contains a portion having an identical        known contents of bits (denoted a training sequence). However,        note that a different embodiment can use (e.g., 70) received        bursts containing different (non-identical) information, but        information still known to the RX.;    -   (b) receiving the “M” signal samples s_(i) along with multipath        components and noise at, e.g., RX;    -   (c) for each of the received “M” samples s_(i), determining at        the RX an estimated channel power profile (CPPi). Each CPPi is        determined by first determining, via a processor at the RX, a        combined correlation response (“Channel Impulse Response” or        CIRi) of a small number of the bursts (e.g., 5) by correlating        each burst with its known contents. Accordingly; the squared        absolute value of the CIRi is the “estimated channel power        profile” or CPPi;    -   (d) (randomly) selecting “N” (e.g., 10) out of the “M” received        samples;    -   (e) performing incoherent integration of the CPPi for the “N”        samples selected, which results in an integrated signal, i.e.,        one integrated channel power profile_ICPP(Ni);    -   (f) determining if the signal-to-noise quality of the ICPP(Ni)        is greater than or equal to a predetermined threshold value, and        if not, improving the signal-to-noise quality of ICPP(Ni) as        required, by redoing the incoherent integration with        successively one additional received sample CPPi until the        signal-to-noise quality of the ICPP(Ni) is greater than or equal        to the predetermined threshold value;    -   (g) determining the TOA(i), including the case of determining        TOA(i) from the maximum signal amplitude;    -   (h) entering the determined TOA(i) value into a diagram that        shows a frequency of occurrence as a function of TOA(i);    -   (i) repeating the whole procedure “X” times by selecting a new        combination of “N” out of “M” samples, which results in “X”        additional points in the frequency of occurrence diagram;    -   (j) reading the minimum value TOA(min) as the time value having        “z” of all occurrences with higher TOA(i) values and “1−z” of        all occurrences with lower TOA(i) values, where z>0.7.

As mentioned above, an embodiment of the Grubeck FOM may also beprovides for TDOA and/or AOA wireless location techniques, wherein asimilar incoherent integration may be performed.

Note that a Grubeck FOM may be particularly useful for locating a targetMS 140 in a GSM wireless network.

TCSO (Parl) FOM Using Different Tones and Multiple Antennas at BSs 122

A first order model 1224, denoted the Parl model herein, issubstantially disclosed in U.S. Pat. No. 5,883,598 (denoted the '598patent herein) filed Dec. 15, 1995 and issued Mar. 16, 1999 having Parl,Bussgang, Weitzen and Zagami as inventors, this patent being fullyincorporated herein by reference. The Parl FOM includes a system forreceiving representative signals (denoted also “locating signal(s)”)from the target MS 140 via, e.g., base stations 122 and subsequentlycombines information regarding the amplitude and phase of the MStransmitted signals received at the base stations to determine theposition of the target MS 140. In one embodiment, the Parl model usesinput from a locating signal having two or more single-frequency tones,as one skilled in the art will understand. Moreover, at least some ofthe base stations 122 preferably includes at least two antennas spacedfrom each other by a distance between a quarter wavelength and severalwavelengths of the wireless locating signals received from the target MS140. Optionally, another antenna vertically above or below the two ormore antennas also spaced by a distance of between a quarter wavelengthand several wavelengths can be used where elevation is also beingestimated. The base stations 122 sample locating signals from the targetMS 140. The locating signals include tones that can be at differentfrequencies. The tones can also be transmitted at different times, or,in an alternative embodiment, they can be transmitted simultaneously.Because, in one embodiment, only single-frequency tones are used as thelocating signal instead of modulated signals, substantial transmissioncircuitry may be eliminated. The Parl FOM extracts information from eachrepresentative signal received from a target MS 144, wherein at leastsome of the extracted information is related to the amplitude and phaseof the received signal.

In one embodiment of a Parl FOM, related to the disclosure in the '598patent, when the locations of the BSs 122 are known, and the directionfrom any two of the BSs 122 to the target MS 140, the MS's location canbe initially (roughly) determined by signal direction findingtechniques. For example, an estimate of the phase difference between thesignals at a pair of antennas at any BS 122 (having two such antennas)can lead to the determination of the angle from the base station to thetarget MS 140, and thus, the determination of the target MS direction.Subsequently, an enhanced location of the target MS 140 is computeddirectly from received target MS signal data using an ambiguity functionA(x,y) described in the '598 patent, wherein for each point at x,y, theambiguity function A(x,y) depends upon the probability that the MS islocated at the geolocation represented by (x,y). Essentially the ParlFOM combines angle of arrival related data and TDOA related data forobtaining an optimized estimate of the target MS 140. However, itappears that independent AOA and TDOA MS locations are not used indetermining a resulting target MS location (e.g., without the need forprojecting lines at angles of arrival or computing the intersection ofhyperbolas defined by pairs of base stations). Instead, the Parl FOMestimates the target MS's location by minimizing a joint probability oflocation related errors. In particular, such minimization may use themean square error, and the location (x,y) at which minimization occursis taken as the estimate of the target MS 140. In particular, theambiguity function A(x,y) defines the error involved in a positiondetermination for each point in a geolocation Cartesian coordinatesystem. The Parl model optimizes the ambiguity function to select apoint x,y at which the associated error is minimized. The resultinglocation for (x, y) is taken as the estimate of the location of thetarget MS 140. Any of several different optimization procedures can beused to optimize the ambiguity function A(x,y). E.g., a first roughestimate of the target MS's location may be obtained by directionfinding (as discussed above). Next, six points x,y may be selected thatare in close proximity to the estimated point. The ambiguity functionA(x,y) is solved for each of the x,y points to obtain six values. Thesix computed values are then used to define a parabolic surface. Thepoint x,y at which the maximum value of the parabolic surface occurs isthen taken as the estimate of the target MS 140. However, otheroptimization techniques may also be used. For example, a standardtechnique such as an iterative progression through trial and error toconverge to the maximum can be used. Also, gradient search can be usedto optimize the ambiguity function. In the case of three-dimensionallocation, the two-dimensional ambiguity function A(x,y) is extended to athree-dimensional function A(x,y,z). As in the two-dimensional case, theambiguity function may be optimized to select a point x,y,z as the bestestimate of the target MS's location in three dimensions. Again, any ofseveral known optimization procedures, such as iterative progressionthrough trial and error, gradient search, etc., can be used to optimizethe ambiguity function.

TCSO FOM Using TDOA/AOA Measurements from an MBS 148 and/or an LBS 152

It is believed clear from the location center/gateway 142 architectureand from the architecture of the mobile station location subsystem(described in a separate section hereinbelow) that target MS 140location related information can be obtained from an MBS 148 and/or oneor more LBSs 152. Moreover, such location related information can besupplied to any FOM 1224 that is able to accept such information asinput. Thus, pattern recognition and adaptive FOMs may accept suchinformation. However, to provide an alternative description of how MSlocation related information from an MBS and/or LBS may be used,reference is made to U.S. Pat. No. 6,031,490 (denoted the '490 patentherein) filed Dec. 23, 1997 and issued Feb. 29, 2000 having Forssen,Berg and Ghisler as inventors, this patent being fully incorporatedherein fully by reference. A TCSO FOM (denoted the FORSSEN FOM herein)using TDOA/AOA is disclosed in the '490 patent.

The FORSSEN FOM includes a location estimator for determining the TimeDifference of Arrival (TDOA) of the position of a target MS 140, whichis based on Time of Arrival (TOA) and/or AOA measurements. This FOM usesdata received from “measuring devices” provided within a wirelesstelecommunications network. The measuring devices measure TOA on demandand (optionally) Direction of Arrival (DOA), on a digital uplink timeslot or on digital information on an analog uplink traffic channel inone or more radio base stations. The TOA and DOA information and thetraffic channel number are reported to a Mobile Services SwitchingCenter (MSC), which obtains the identity of the target MS 140 from thetraffic channel number and sends the terminal identity and TOA and DOAmeasurement information to a Service Node (e.g., location center 142) ofthe network. The Service Node calculates the position of the target MS140 using the TOA information (supplemented by the DOA information whenavailable). Note, that the TCSO model may utilize data from a secondmobile radio terminal is colocated on a mobile platform (auto, emergencyvehicle, etc.) with one of the radio base stations (e.g., MBS 148),which can be moved into relatively close proximity with the target MS140. Consequently, by moving one of the radio base stations (MBSs) closeto the region of interest (near the target MS 140), the positiondetermination accuracy is significantly improved.

Note that the '490 patent also discloses techniques for rising thetarget MS's transmission power for thereby allowing wireless signalsfrom the target MS to be better detected by distant BSs 122.

Coverage Area First Order Model

Radio coverage area of individual base stations 122 may be used togenerate location estimates of the target MS 140. Although a first ordermodel 1224 based on this notion may be less accurate than othertechniques, if a reasonably accurate RF coverage area is known for each(or most) of the base stations 122, then such a FOM (denoted hereinafteras a “coverage area first order model” or simply “coverage area model”)may be very reliable. To determine approximate maximum radio frequency(RF) location coverage areas, with respect to BSs 122, antennas and/orsector coverage areas, for a given class (or classes) of (e.g., CDMA orTDMA) mobile station(s) 140, location coverage should be based on anMS's ability to adequately detect the pilot channel, as opposed toadequate signal quality for purposes of carrying user-acceptable trafficin the voice channel. Note that more energy is necessary for trafficchannel activity (typically on the order of at least −94 to −104 dBmreceived signal strength) to support voice, than energy needed to simplydetect a pilot channel's presence for location purposes (typically amaximum weakest signal strength range of between −104 to −110 dBm), thusthe “Location Coverage Area” will generally be a larger area than thatof a typical “Voice Coverage Area”, although industry studies have foundsome occurrences of “no-coverage” areas within a larger covered area

The approximate maximum RF coverage area for a given sector of (moregenerally angular range about) a base station 122 may be represented asa set of points representing a polygonal area (potentially with, e.g.,holes therein to account for dead zones and/or notches). Note that ifsuch polygonal RF coverage area representations can be reliablydetermined and maintained over time (for one or more BS signal powerlevel settings), then such representations can be used in providing aset theoretic or Venn diagram approach to estimating the location of atarget MS 140. Coverage area first order models utilize such anapproach.

One embodiment, a coverage area model utilizes both the detection andnon-detection of base stations 122 by the target MS 140 (conversely, ofthe MS by one or more base stations 122) to define an area where thetarget MS 140 may likely be. A relatively straightforward application ofthis technique is to:

-   -   (a) find all areas of intersection for base station RF coverage        area representations, wherein: (i) the corresponding base        stations are on-line for communicating with MSs 140; (ii) the RF        coverage area representations are deemed reliable for the power        levels of the on-line base stations; (iii) the on-line base        stations having reliable coverage area representations can be        detected by the target MS; and (iv) each intersection must        include a predetermined number of the reliable RF coverage area        representations (e.g., 2 or 3); and    -   (b) obtain new location estimates by subtracting from each of        the areas of intersection any of the reliable RF coverage area        representations for base stations 122 that can not be detected        by the target MS.

Accordingly, the new areas may be used to generate location hypotheses.

Satellite Signal Triangulation First Order Models

As mentioned hereinabove, there are various satellite systems that maybe used to provide location estimates of a target MS 140 (e.g., GPS,GLONASS, LEOs, and MEOs). In many cases, such location estimates can bevery accurate, and accordingly such accuracy would be reflected inembodiments of the present disclosure by relatively high confidencevalues for the location hypotheses generated from such models incomparison to other FOMs. However, it may be difficult for the target MS140 to detect and/or lock onto such satellite signals sufficiently wellto provide a location estimate. For example, it may be very unlikelythat such satellite signals can be detected by the MS 140 in the middleof high rise concrete buildings or parking structures having veryreduced exposure to the sky.

Hybrid Satellite and TCSO FOMs

A first order model 1224, denoted the WATTERS FOM herein, is disclosedin U.S. Pat. No. 5,982,324 filed May 14, 1998 and issued Nov. 9, 1999having Watters, Strawczynski, and Steer as inventors, this patent beingfully incorporated herein by reference. The WATTERS FOM includes alocation estimator for determining the location of a target MS 140 usingsatellite signals to the target MS 140 as well as delay in wirelesssignals communicated between the target MS and base stations 122. Forexample, aspects of global positioning system (GPS) technology andcellular technology are combined in order to locate a target MS 140. TheWATTERS FOM may be used to determine target MS location in a wirelessnetwork, wherein the network is utilized to collect differential GPSerror correction data, which is forwarded to the target MS 140 via thewireless network. The target MS 140 (which includes a receiver R forreceiving non-terrestrial wireless signals from, e.g., GPS, or othersatellites, or even airborne craft) receives this data, along with GPSpseudoranges using its receiver R, and calculates its position usingthis information. However, when the requisite number of satellites arenot in view of the MS 140, then a pseudosatellite signal, broadcast froma BS 122 of the wireless network, is received by the target MS 140 andprocessed as a substitute for the missing satellite signal.Additionally, in at least some circumstances, when the requisite numberof satellites (more generally, non-terrestrial wireless transmitters)are not detected by the receiver R, then the target MS's location iscalculated using the wireless network infrastructure via TDOA/TOA withthe BSs 122 of the network. When the requisite number of satellites(more generally, non-terrestrial wireless transmitters) are againdetected by the receiver R, then the target MS is again calculated usingwireless signals from the non-terrestrial wireless transmitters.Additionally, the WATTERS FOM may use wireless signals already beingtransmitted from base stations 122 to the target MS 140 in a wirelessnetwork to calculate a round trip time delay, from which a distancecalculation between the base station and the target MS can be made. Thisdistance calculation substitutes for a missing non-terrestrialtransmission signal.

Location Base Station First Order Model

In the location base station (LBS) model (FOM 1224), a database isaccessed which contains electrical, radio propagation and coverage areacharacteristics of each of the location base stations in the radiocoverage area. The LBS model is an active model, in that it can probe orexcite one or more particular LBSs 152 in an area for which the targetMS 140 to be located is suspected to be placed. Accordingly, the LBSmodel may receive as input a most likely target MS 140 location estimatepreviously output by the location engine 139 of the present disclosure,and use this location estimate to determine which (if any) LBSs 152 toactivate and/or deactivate for enhancing a subsequent location estimateof the target MS. Moreover, the feedback from the activated LBSs 152 maybe provided to other FOMs 1224, as appropriate, as well as to the LBSmodel. However, it is an important aspect of the LBS model that when itreceives such feedback, it may output location hypotheses havingrelatively small target MS 140 location area estimates about the activeLBSs 152 and each such location hypothesis also has a high confidencevalue indicative of the target MS 140 positively being in thecorresponding location area estimate (e.g., a confidence value of 0.9 to+1), or having a high confidence value indicative of the target MS 140not being in the corresponding location area estimate (i.e., aconfidence value of −0.9 to −1). Note that in some embodiments of theLBS model, these embodiments may have functionality similar to that ofthe coverage area first order model described above. Further note thatfor LBSs within a neighborhood of the target MS wherein there is areasonable chance that with movement of the target MS may be detected bythese LBSs, such LBSs may be requested to periodically activate. (Note,that it is not assumed that such LBSs have an on-line external powersource; e.g., some may be solar powered). Moreover, in the case where anLBS 152 includes sufficient electronics to carry voice communicationwith the target MS 140 and is the primary BS for the target MS (oralternatively, in the active or candidate set), then the LBS model willnot deactivate this particular LBS during its procedure of activatingand deactivating various LBSs 152.

Stochastic First Order Model

The stochastic first order models may use statistical predictiontechniques such as principle decomposition, partial least squares,partial least squares, or other regression techniques for predicting,for example, expected minimum and maximum distances of the target MSfrom one or more base stations 122, e.g., Bollenger Bands. Additionally,some embodiments may use Markov processes and Random Walks (predictedincremental MS movement) for determining an expected area within whichthe target MS 140 is likely to be. That is, such a process measures theincremental time differences of each pilot as the MS moves forpredicting a size of a location area estimate using past MS estimatessuch as the verified location signatures in the location signature database 1320.

Pattern Recognition and Adaptive First Order Models

It is a particularly important aspect of the present disclosure toprovide:

-   -   (a) one or more FOMs 1224 that generate target MS 140 location        estimates by using pattern recognition or associativity        techniques, and/or    -   (b) one or more FOMs 1224 that are adaptive or trainable so that        such FOMs may generate increasingly more accurate target MS        location estimates from additional training.        Statistically Based Pattern Recognition First Order Models

Regarding FOMs 1224 using pattern recognition or associativitytechniques, there are many such techniques available. For example, thereare statistically based systems such as “CART” (acronym forClassification and Regression Trees) by ANGOSS Software InternationalLimited of Toronto, Canada that may be used for automatically fordetecting or recognizing patterns in data that were not provided (andlikely previously unknown). Accordingly, by imposing a relatively finemesh or grid of cells of the radio coverage area, wherein each cell isentirely within a particular area type categorization, such as thetransmission area types (discussed in the section, “Coverage Area: AreaTypes And Their Determination” above), the verified location signatureclusters within the cells of each area type may be analyzed for signalcharacteristic patterns. Accordingly, if such a characteristic patternis found, then it can be used to identify one or more of the cells inwhich a target MS is likely to be located. That is, one or more locationhypotheses may be generated having target MS 140 location estimates thatcover an area having the identified cells wherein the target MS 140 islikely to be located. Further note that such statistically based patternrecognition systems as “CART” include software code generators forgenerating expert system software embodiments for recognizing thepatterns detected within a training set (e.g., the verified locationsignature clusters).

A related statistical pattern recognition FOM 1224 is also disclosed inU.S. Pat. No. 6,026,304, filed Jan. 8, 1997 and issued Feb. 15, 2000,having Hilsenrath and Wax as inventors, this patent (denoted theHilsenrath patent herein) being incorporated herein fully by reference.An embodiment of a FOM 1224 based on the disclosure of the Hilsenrathpatent is referred to herein as the Hilsenrath FOM. The Hilsenrath FOMincludes a wireless location estimator that locates a target MS 140using measurements of multipath signals in order to accurately determinethe location of the target MS 140. More particularly, to locate thetarget MS 140, the Hilsenrath FOM uses wireless measurements of both adirect signal transmission path and multipath transmission signals fromthe MS 140 to a base station 122 receiver. The wireless signals from thetarget MS 140 arrive at and are detected by an antenna array of thereceiver at the BS 122, wherein the antenna array includes a pluralityof antennas. A signal signature (e.g., an embodiment of a locationsignature herein) for this FOM may be derived from any combination ofamplitude, phase, delay, direction, and polarization information of thewireless signals transmitted from the target MS 140 to the base station122 receiver. The Hilsenrath FOM 1224 determines a signal signature froma signal subspace of a covariance matrix. In particular, for p antennasincluded in the base station receiver, these antennas are used toreceive complex signal envelopes x.₁(t), x.₂ (t), . . . , x._(p) (t),respectively, which are conventionally grouped together to form ap-dimensional array vector x(t)=[x₁(t), x₂(t), . . . , x._(p) (t)]^(T).The signal subspace may be determined from a collection of M such arrayvectors x(t) by several techniques. In one such technique, the outerproducts of the M vectors are added together to form a p×p signalcovariance matrix, R=1/M [x(t₁)x(t₁)^(H)+ . . . +x(t_(M))x(t_(M))”]. Theeigenvalues of R whose magnitudes exceed a predetermined thresholddetermine a set of dominant eigenvectors. The signal subspace is thespace spanned by these dominant eigenvectors. The signal signature iscompared to a database of calibrated signal signatures and correspondinglocations (e.g., an embodiment of the location signature data base1320), wherein the signal signatures in the database includerepresentations of the signal subspaces (such as the dominanteigenvectors of the covariance matrices. Accordingly, a location whosecalibrated signature best matches the signal signature of the target MS140 is selected as the most likely location of the target MS 140. Notethat the database of calibrated signal signatures and correspondingverified locations is generated by a calibration procedure in which acalibrating MS 140 transmits location data derived from a co-located GPSreceiver to the base stations 122. Thus, for each of a plurality oflocations distributed through a service area, the location hasassociated therewith: the (GPS or verified) location information and thecorresponding signal signature of the calibrating MS 140.

Accordingly, the location of a target MS 140 in the service area may bedetermined as follows. Signals originating from the target MS 140 at anunknown location are received at a base station 122. A signal processor,e.g., at the base station 122, then determines the signal signature asdescribed above. The signal signature is then compared with thecalibrated signal signatures stored in the above described embodiment ofthe location signature database 1320 during the calibration procedure.Using a measure of difference between subspaces (e.g., an angle betweensubspaces), a set of likely locations is selected from this locationsignature database embodiment. These selected likely locations are thoselocations whose associated calibrated signal signatures differ by lessthan a minimum threshold value from the target MS 140 signal signature.The difference measure is further used to provide a correspondingmeasure of the probability that each of the selected likely locations isthe actual target MS location. Moreover, for one or more of the selectedlikely location, the corresponding measure may be output as theconfidence value for a corresponding location hypothesis output by aHilsenrath FOM 1224.

Thus, an embodiment of the present disclosure using such a HilsenrathFOM 1224 performs the following steps (a)-(d):

-   -   (a) receiving at an antenna array provided at one of the base        stations 122, signals originating from the target MS 140,        wherein the signals comprise p-dimensional array vectors sampled        from p antennas of the array;    -   (b) determining from the received signals, a signal signature,        wherein the signal signature comprises a measured subspace,        wherein the array vectors x(t) are approximately confined to the        measured subspace;    -   (c) comparing the signal signature to previously obtained (and        similarly computed) signal signatures, wherein each of the        previously obtained signal signatures, SS, has associated        therewith corresponding location data verifying the location        where SS was obtained, wherein this step of comparing comprises        substep of calculating differences between: (i) the measured        subspace, and (ii) a similarly determined subspace for each of a        plurality of the previously obtained signal signatures; and    -   (d) selecting from the previously obtained signal signatures a        most likely signal signature and a corresponding most likely        location of the target MS 140 by using the calculated        differences;

Note that regardless of the reliability some FOMs as described here maynot be exceedingly accurate, but may be very reliable. Thus, since anaspect of at least some embodiments of the present disclosure is to usea plurality of MS location techniques (FOMs) for generating locationestimates and to analyze the generated estimates (likely after beingadjusted) to detect patterns of convergence or clustering among theestimates, even large MS location area estimates may be useful. Forexample, it can be the case that four different and relatively large MSlocation estimates, each having very high reliability, have an area ofintersection that is acceptably precise and inherits the very highreliability from each of the large MS location estimates from which theintersection area was derived.

Note, that another statistically based FOM 1224 may be provided whereinthe radio coverage area is decomposed substantially as above, but inaddition to using the signal characteristics for detecting useful signalpatterns, the specific identifications of the base station 122 providingthe signal characteristics may also be used. Thus, assuming there is asufficient density of verified location signature clusters in some ofthe mesh cells so that the statistical pattern recognizer can detectpatterns in the signal characteristic measurements, an expert system maybe generated that outputs a target MS 140 location estimate that mayprovide both a reliable and accurate location estimate of a target MS140.

Adaptive/Trainable First Order Models

The term adaptive is used to describe a data processing component thatcan modify its data processing behavior in response to certain inputsthat are used to change how subsequent inputs are processed by thecomponent. Accordingly, a data processing component may be “explicitlyadaptive” by modifying its behavior according to the input of explicitinstructions or control data that is input for changing the component'ssubsequent behavior in ways that are predictable and expected. That is,the input encodes explicit instructions that are known by a user of thecomponent. Alternatively, a data processing component may be “implicitlyadaptive” in that its behavior is modified by other than instructions orcontrol data whose meaning is known by a user of the component. Forexample, such implicitly adaptive data processors may learn by trainingon examples, by substantially unguided exploration of a solution space,or other data driven adaptive strategies such as statistically generateddecision trees. Accordingly, it is an aspect of the present disclosureto utilize not only explicitly adaptive MS location estimators withinFOMs 1224, but also implicitly adaptive MS location estimators. Inparticular, artificial neural networks (also denoted neural nets andANNs herein) are used in some embodiments as implicitly adaptive MSlocation estimators within FOMs. Thus, in the sections below, neural netarchitectures and their application to locating an MS is described.

Artificial Neural Networks For MS Location

Artificial neural networks may be particularly useful in developing oneor more first order models 1224 for locating an MS 140, since, forexample, ANNs can be trained for classifying and/or associativelypattern matching of various RF signal measurements such as the locationsignatures. That is, by training one or more artificial neural netsusing RF signal measurements from verified locations so that RF signaltransmissions characteristics indicative of particular locations areassociated with their corresponding locations, such trained artificialneural nets can be used to provide additional target MS 140 locationhypotheses. Moreover, it is an aspect of the present disclosure that thetraining of such artificial neural net based FOMs (ANN FOMs) is providedwithout manual intervention as will be discussed hereinbelow. Additionaldescription of this aspect of the present disclosure can be found in thecopending U.S. patent application titled “Location Of A Mobile Station”filed Nov. 24, 1999 having application Ser. No. 09/194,367 whoseinventors are D. J. Dupray and C. L. Karr, which is incorporated hereinby reference and wherein this copending patent application may haveessential material for the present disclosure. In particular, thiscopending patent application may have essential material relating to theuse of ANNs as mobile station location estimators 1224.

Other First Order Models

U.S. Pat. No. 5,390,339 ('339 patent) filed Oct. 23, 1991 having anissue date of Feb. 14, 1995 with inventor being Bruckert et. al.provides number of embodiments of wireless location estimators forestimating the location of a “remote unit.” In particular, variouslocation estimator embodiments are described in relation to FIGS. 1B and2B therein. The location estimators in the '339 patent are, in general,directed to determining weighted or adjusted distances of the “remoteunit” (e.g., MS 140) from one or more “transceivers” (e.g., basestations 122). The distances are determined using signal strengthmeasurements of wireless signals transmitted between the “remote unit”and the “ransceivers.” However, adjustments are in the signal strengthsaccording to various signal transmission anomalies (e.g., co-channelinterference), impairments and/or errors. Additionally, a signal RFpropagation model may be utilized, and a likelihood of the “remote unit”being in the designated coverage areas (cells) of particulartransceivers (e.g., base stations 122) is determined using probabilistictechniques such as posteriori probabilities. Accordingly, the Bruckert'339 patent is fully incorporated by reference herein.

U.S. Pat. No. 5,570,412 ('412 patent) filed Sep. 28, 1994 having anissue date of Oct. 29, 1996 with inventors LeBlanc et. al. providefurther embodiments of wireless location estimators that may be used asFirst Order Models 1224. The location estimating techniques of theLeBlanc '412 patent are described with reference to FIG. 8 andsucceeding figures therein. At a high level, wireless locationtechniques of the '412 patent can be characterized by the followingquote therefrom:

-   -   “The location processing of the present invention focuses on the        ability to predict and model RF contours using actual RF        measurements, then performing data reduction techniques such as        curve fitting technique, Bollinger Bands, and Genetic        Algorithms, in order to locate a mobile unit and disseminate its        location.”        Accordingly, the LeBlanc '412 patent is fully incorporated by        reference herein.

U.S. Pat. No. 5,293,645 ('645 patent) filed Oct. 4, 1991 having an issuedate of Mar. 8, 1994 with inventor Sood. provide further embodiments ofwireless location estimators that may be used as First Order Models1224. In particular, the '645 patent describes wireless locationestimating techniques using triangulations or other geographicalintersection techniques. Further, one such technique is described incolumn 6, line 42 through column 7, line 7. Accordingly, the Sood '645patent is fully incorporated by reference herein.

U.S. Pat. No. 5,293,642 ('642 patent) filed Dec. 19, 1990 having anissue data of Mar. 8, 1994 with inventor Lo provide further embodimentsof wireless location estimators that may be used as First Order Models1224. In particular, the '642 patent determines a correspondingprobability density function (pdf) about each of a plurality of basestations in communication with the target MS 140. That is, uponreceiving wireless signal measurements from the transmissions betweenthe target MS 140 and base stations 122, for each BS 122, acorresponding pdf is obtained from prior measurements of a particularwireless signal characteristic at locations around the base station.Subsequently, a most likely location estimation is determined from ajoint probability density function of the individual base stationprobability density functions. Further description can be found in theDescription Of The Preferred Embodiment section of the '642 patent.Accordingly, the Lo '642 patent is incorporated by reference herein.

Hybrid First Order Models

Time Difference of Arrival and Timing Advance FOM

A first order model 1224 denoted the Yost model herein. The Yost modelincludes a location estimator that uses a combination of Time Differenceof Arrival (TDOA) and Timing Advance (TA) location determiningtechniques for determining the location of a target MS 140, whereinthere are minor modifications to a telecommunication network such as aCMRS. The hybrid wireless location technique utilized by this locationestimator uses TDOA measurements and TA measurements to obtainsubstantially independent location estimates of the target MS 140,wherein the TDOA measurements determine hyperbolae MS loci, about basestations 122 communicating (uni or bi-directionally) with the target MS,and the TA measurements determine circles about the base stations 122.Accordingly, an enhanced location estimate of the MS 140 can be obtainedby using a least squares (or other statistical technique), wherein theleast-squares technique determines a location for the MS between thevarious curves (hyperbolae and circles) that best approximates a pointof intersection. Note that TA is used in all Time Division MultipleAccess (TDMA) systems as one skilled in the art will understand, andmeasurements of TA can provide a measurement of the distance of the MSfrom a TDMA communication station in communication with the target MS140. The Yost model is disclosed in U.S. Pat. No. 5,987,329 ('329patent) filed Jul. 30, 1997 and issued Nov. 16, 1999 having Yost andPanchapakesan as inventors, this patent being fully incorporated hereinfully by reference to thereby further describe the Yost model. Thefollowing quote from the '329 patent describes an important aspect ofthe Yost model:

-   -   “Furthermore, the combination of TA and TDOA allows resolution        of common ambiguities suffered by either technique separately.        For example, in FIG. 5 a situation involving three base stations        24 (A, B and C as described, the latter being visible in the        figure) is represented along with the resultant two hyperbolas        AB and AC (and redundant hyperbola BC) for a TDOA position        determination of the mobile M. FIG. 5 is a magnified view of the        mobile terminal M location showing the nearby base stations and        the nearby portions at the curves. It should be understood that,        in this case, using TDOA alone, there are two possible        solutions, where the hyperbolae cross. The addition of the TA        circles (dashed curves) will allow the ambiguous solutions,        which lie at different TA from all three base stations, to be        clearly resolved without the need for additional base station 24        measurements.”

As an aside note that a timing advance (TA) first order model may beprovided as a separate FOM independent from the TDOA portion of the Yostmodel. Thus, if an embodiment of the present disclosure includes both aTA FOM and a TDOA FOM, then the multiple location estimator architectureof the present disclosure may substantially include the Yost modelwhenever both the TA FOM and TDOA FOM are both activated for a samelocation instance of a target MS 140. However, it is an aspect of thepresent disclosure to also activate such a TA FOM and a TDOA FOMasynchronously from one another.

Satellite and Terrestrial Base Station Hybrid FOM

A first order model 1224, denoted the Sheynblat model (FOM) herein, isdisclosed in U.S. Pat. No. 5,999,124 (denoted the '124 patent herein)filed Apr. 22, 1998 and issued Dec. 7, 1999 having Sheynblat as theinventor, this patent being fully incorporated herein by reference TheSheynblat FOM provides a location estimator for processing target MS 140location related information obtained from: (a) satellite signals of asatellite positioning system (denoted SPS in the '124 patent) (e.g., GPSor GLONASS, LEO positioning satellites, and/or MEO positioningsatellites), and (b) communication signals transmitted in theterrestrial wireless cellular network of BSs 122 for a radio coveragearea, e.g., coverage area 120 (FIG. 4), wherein there is two-waywireless communication between the target MS 140 and the BSs. In oneembodiment of the Sheynblat FOM, the location related informationobtained from the satellite signals includes a representation of a timeof travel of SPS satellite signals from a SPS satellite to acorresponding SPS receiver operatively coupled to (and co-located with)the target MS 140 (such “time of travel” is referred to as a pseudorangeto the SPS satellite), Additionally for this embodiment, the locationrelated information obtained from the communication signals in thewireless cellular network includes time of travel related informationfor a message in the communication signals between a BS 122 transceiverand the target MS 140 (this second “time of travel” related informationis referred to as a cellular pseudorange). Accordingly, variouscombinations of pseudoranges to SPS satellites, and cellularpseudoranges can be used to determine a likely location of the target MS140. As an example, if the target MS 140 (enhanced with a SPS receiver)can receive SPS satellite signals from one satellite, and additionally,the target MS is also in wireless communication (or can be in wirelesscommunication) with two BSs 122, then three pseudoranges may be obtainedand used to determine the position of the target MS by, e.g.,triangulation. Of course, other combinations are possible fordetermining a location of the target MS 140, e.g., pseudoranges to twoSPS satellites and one cellular pseudorange. Additionally, varioustechniques may be used to mitigate the effects of multipath on thesepseudoranges. For example, since it is typical for the target MS 140 todetect (or be detected by) a plurality of BSs 122, a correspondingplurality of cellular pseudoranges may be obtained, wherein suchcellular psuedoranges may be used in a cluster analysis technique todisambiguate MS locations identified by the satellite pseudoranges.Moreover, the determination of a location hypothesis is performed, in atleast one embodiment, at a site remote from the target MS 140, such asthe location center/gateway 142, or another site that communicates withthe location center/gateway for supplying a resulting MS location to thegateway. Alternatively, the target MS 140 may perform the calculationsto determine its own location. Note that this alternative technique maybe particularly useful when the target MS 140 is a mobile base station148.

MS Status Repository Embodiment

The MS status repository 1338 is a run-time storage manager for storinglocation hypotheses from previous activations of the location engine 139(as well as the output target MS location estimate(s)) so that a targetMS may be tracked using target MS location hypotheses from previouslocation engine 139 activations to determine, for example, a movement ofthe target MS between evaluations of the target MS location. Thus, byretaining a moving window of previous location hypotheses used inevaluating positions of a target MS, measurements of the target MS'svelocity, acceleration, and likely next position may be determined bythe location hypothesis analyzer 1332. Further, by providingaccessibility to recent MS location hypotheses, these hypotheses may beused to resolve conflicts between hypotheses in a current activation forlocating the target MS; e.g., MS paths may be stored here for use inextrapolating a new location

Mobile Base Station Location Subsystem Description

Mobile Base Station Subsystem Introduction

Any collection of mobile electronics (denoted mobile location unit) thatis able to both estimate a location of a target MS 140 and communicatewith the base station network may be utilized by embodiments of thepresent disclosure to more accurately locate the target MS. Such mobilelocation units may provide greater target MS location accuracy by, forexample, homing in on the target MS and by transmitting additional MSlocation information to the location center 142. There are a number ofembodiments for such a mobile location unit contemplated by the presentdisclosure. For example, in a minimal version, such the electronics ofthe mobile location unit may be little more than an onboard MS 140, asectored/directional antenna and a controller for communicating betweenthem. Thus, the onboard MS is used to communicate with the locationcenter 142 and possibly the target MS 140, while the antenna monitorssignals for homing in on the target MS 140. In an enhanced version ofthe mobile location unit, a GPS receiver may also be incorporated sothat the location of the mobile location unit may be determined andconsequently an estimate of the location of the target MS may also bedetermined. However, such a mobile location unit is unlikely to be ableto determine substantially more than a direction of the target MS 140via the sectored/directional antenna without further base stationinfrastructure cooperation in, for example, determining the transmissionpower level of the target MS or varying this power level. Thus, if thetarget MS or the mobile location unit leaves the coverage area 120 orresides in a poor communication area, it may be difficult to accuratelydetermine where the target MS is located. None-the-less, such mobilelocation units may be sufficient for many situations, and in fact thepresent disclosure contemplates their use. However, in cases wheredirect communication with the target MS is desired without constantcontact with the base station infrastructure, the present disclosureincludes a mobile location unit that is also a scaled down version of abase station 122. Thus, given that such a mobile base station or MBS 148includes at least an onboard MS 140, a sectored/directional antenna, aGPS receiver, a scaled down base station 122 and sufficient components(including a controller) for integrating the capabilities of thesedevices, an enhanced autonomous MS mobile location system can beprovided that can be effectively used in, for example, emergencyvehicles, air planes and boats. Accordingly, the description thatfollows below describes an embodiment of an MBS 148 having the abovementioned components and capabilities for use in a vehicle.

As a consequence of the MBS 148 being mobile, there are fundamentaldifferences in the operation of an MBS in comparison to other types ofBS's 122 (152). In particular, other types of base stations have fixedlocations that are precisely determined and known by the locationcenter, whereas a location of an MBS 148 may be known only approximatelyand thus may require repeated and frequent re-estimating. Secondly,other types of base stations have substantially fixed and stablecommunication with the location center (via possibly other BS's in thecase of LBSs 152) and therefore although these BS's may be more reliablein their in their ability to communicate information related to thelocation of a target MS with the location center, accuracy can beproblematic in poor reception areas. Thus, MBSs may be used in areas(such as wilderness areas) where there may be no other means forreliably and cost effectively locating a target MS 140 (i.e., there maybe insufficient fixed location BS's coverage in an area).

FIG. 11 provides a high level block diagram architecture of oneembodiment of the MBS location subsystem 1508. Accordingly, an MBS mayinclude components for communicating with the fixed location BS networkinfrastructure and the location center 142 via an on-board transceiver1512 that is effectively an MS 140 integrated into the locationsubsystem 1508. Thus, if the MBS 148 travels through an area having poorinfrastructure signal coverage, then the MBS may not be able tocommunicate reliably with the location center 142 (e.g., in rural ormountainous areas having reduced wireless telephony coverage). So it isdesirable that the MBS 148 must be capable of functioning substantiallyautonomously from the location center. In one embodiment, this impliesthat each MBS 148 must be capable of estimating both its own location aswell as the location of a target MS 140.

Additionally, many commercial wireless telephony technologies requireall BS's in a network to be very accurately time synchronized both fortransmitting MS voice communication as well as for other services suchas MS location. Accordingly, the MBS 148 will also require such timesynchronization. However, since an MBS 148 may not be in constantcommunication with the fixed location BS network (and indeed may beoff-line for substantial periods of time), on-board highly accuratetiming device may be necessary. In one embodiment, such a device may bea commercially available ribidium oscillator 1520 as shown in FIG. 11.

Since the MBS 148, includes a scaled down version of a BS 122 (denoted1522 in FIG. 11), it is capable of performing most typical BS 122 tasks,albeit on a reduced scale. In particular, the base station portion ofthe MBS 148 can:

-   -   (a) raise/lower its pilot channel signal strength,    -   (b) be in a state of soft hand-off with an MS 140, and/or    -   (c) be the primary BS 122 for an MS 140, and consequently be in        voice communication with the target MS (via the MBS operator        telephony interface 1524) if the MS supports voice        communication.        Further, the MBS 148 can, if it becomes the primary base station        communicating with the MS 140, request the MS to raise/lower its        power or, more generally, control the communication with the MS        (via the base station components 1522). However, since the MBS        148 will likely have substantially reduced telephony traffic        capacity in comparison to a standard infrastructure base station        122, note that the pilot channel for the MBS is preferably a        nonstandard pilot channel in that it should not be identified as        a conventional telephony traffic bearing BS 122 by MS's seeking        normal telephony communication. Thus, a target MS 140 requesting        to be located may, depending on its capabilities, either        automatically configure itself to scan for certain predetermined        MBS pilot channels, or be instructed via the fixed location base        station network (equivalently BS infrastructure) to scan for a        certain predetermined MBS pilot channel.

Moreover, the MBS 148 has an additional advantage in that it cansubstantially increase the reliability of communication with a target MS140 in comparison to the base station infrastructure by being able tomove toward or track the target MS 140 even if this MS is in (or movesinto) a reduced infrastructure base station network coverage area.Furthermore, an MBS 148 may preferably use a directional or smartantenna 1526 to more accurately locate a direction of signals from atarget MS 140. Thus, the sweeping of such a smart antenna 1526(physically or electronically) provides directional informationregarding signals received from the target MS 140. That is, suchdirectional information is determined by the signal propagation delay ofsignals from the target MS 140 to the angular sectors of one of moredirectional antennas 1526 on-board the MBS 148.

Before proceeding to further details of the MBS location subsystem 1508,an example of the operation of an MBS 148 in the context of respondingto a 911 emergency call is given. In particular, this example describesthe high level computational states through which the MBS 148transitions, these states also being illustrated in the state transitiondiagram of FIG. 12. Note that this figure illustrates the primary statetransitions between these MBS 148 states, wherein the solid statetransitions are indicative of a typical “ideal” progression whenlocating or tracking a target MS 140, and the dashed state transitionsare the primary state reversions due, for example, to difficulties inlocating the target MS 140.

Accordingly, initially the MBS 148 may be in an inactive state 1700,wherein the MBS location subsystem 1508 is effectively available forvoice or data communication with the fixed location base stationnetwork, but the MS 140 locating capabilities of the MBS are not active.From the inactive state 1700 the MBS (e.g., a police or rescue vehicle)may enter an active state 1704 once an MBS operator has logged onto theMBS location subsystem of the MBS, such logging being forauthentication, verification and journaling of MBS 148 events. In theactive state 1704, the MBS may be listed by a 911 emergency centerand/or the location center 142 as eligible for service in responding toa 911 request. From this state, the MBS 148 may transition to a readystate 1708 signifying that the MBS is ready for use in locating and/orintercepting a target MS 140. That is, the MBS 148 may transition to theready state 1708 by performing the following steps:

-   -   (1a) Synchronizing the timing of the location subsystem 1508        with that of the base station network infrastructure. In one        embodiment, when requesting such time synchronization from the        base station infrastructure, the MBS 148 will be at a        predetermined or well known location so that the MBS time        synchronization may adjust for a known amount of signal        propagation delay in the synchronization signal.    -   (1b) Establishing the location of the MBS 148. In one        embodiment, this may be accomplished by, for example, an MBS        operator identifying the predetermined or well known location at        which the MBS 148 is located.    -   (1c) Communicating with, for example, the 911 emergency center        via the fixed location base station infrastructure to identify        the MBS 148 as in the ready state.

Thus, while in the ready state 1708, as the MBS 148 moves, it has itslocation repeatedly (re)-estimated via, for example, GPS signals,location center 142S location estimates from the base stations 122 (and152), and an on-board deadreckoning subsystem 1527 having an MBSlocation estimator according to the programs described hereinbelow.However, note that the accuracy of the base station time synchronization(via the ribidium oscillator 1520) and the accuracy of the MBS 148location may need to both be periodically recalibrated according to (1a)and (1b) above.

Assuming a 911 signal is transmitted by a target MS 140, this signal istransmitted, via the fixed location base station infrastructure, to the911 emergency center and the location center 142, and assuming the MBS148 is in the ready state 1708, if a corresponding 911 emergency requestis transmitted to the MBS (via the base station infrastructure) from the911 emergency center or the location center, then the MBS may transitionto a seek state 1712 by performing the following steps:

-   -   (2a) Communicating with, for example, the 911 emergency response        center via the fixed location base station network to receive        the PN code for the target MS to be located (wherein this        communication is performed using the MS-like transceiver 1512        and/or the MBS operator telephony interface 1524).    -   (2b) Obtaining a most recent target MS location estimate from        either the 911 emergency center or the location center 142.    -   (2c) Inputting by the MBS operator an acknowledgment of the        target MS to be located, and transmitting this acknowledgment to        the 911 emergency response center via the transceiver 1512.

Subsequently, when the MBS 148 is in the seek state 1712, the MBS maycommence toward the target MS location estimate provided. Note that itis likely that the MBS is not initially in direct signal contact withthe target MS. Accordingly, in the seek state 1712 the following stepsmay be, for example, performed:

-   -   (3a) The location center 142 or the 911 emergency response        center may inform the target MS, via the fixed location base        station network, to lower its threshold for soft hand-off and at        least periodically boost its location signal strength.        Additionally, the target MS may be informed to scan for the        pilot channel of the MBS 148. (Note the actions here are not,        actions performed by the MBS 148 in the “seek state”; however,        these actions are given here for clarity and completeness.)    -   (3b) Repeatedly, as sufficient new MS location information is        available, the location center 142 provides new MS location        estimates to the MBS 148 via the fixed location base station        network.    -   (3c) The MBS repeatedly provides the MBS operator with new        target MS location estimates provided substantially by the        location center via the fixed location base station network.    -   (3d) The MBS 148 repeatedly attempts to detect a signal from the        target MS using the PN code for the target MS.    -   (3e) The MBS 148 repeatedly estimates its own location (as in        other states as well), and receives MBS location estimates from        the location center.

Assuming that the MBS 148 and target MS 140 detect one another (whichtypically occurs when the two units are within 0.25 to 3 miles of oneanother), the MBS enters a contact state 1716 when the target MS 140enters a soft hand-off state with the MBS. Accordingly, in the contactstate 1716, the following steps are, for example, performed:

-   -   (4a) The MBS 148 repeatedly estimates its own location.    -   (4b) Repeatedly, the location center 142 provides new target MS        140 and MBS location estimates to the MBS 148 via the fixed        location base infrastructure network.    -   (4c) Since the MBS 148 is at least in soft hand-off with the        target MS 140, the MBS can estimate the direction and distance        of the target MS itself using, for example, detected target MS        signal strength and TOA as well as using any recent location        center target MS location estimates.    -   (4d) The MBS 148 repeatedly provides the MBS operator with new        target MS location estimates provided using MS location        estimates provided by the MBS itself and by the location center        via the fixed location base station network.

When the target MS 140 detects that the MBS pilot channel issufficiently strong, the target MS may switch to using the MBS 148 asits primary base station. When this occurs, the MBS enters a controlstate 1720, wherein the following steps are, for example, performed:

-   -   (5a) The MBS 148 repeatedly estimates its own location.    -   (5b) Repeatedly, the location center 142 provides new target MS        and MBS location estimates to the MBS 148 via the network of        base stations 122 (152).    -   (5c) The MBS 148 estimates the direction and distance of the        target MS 140 itself using, for example, detected target MS        signal strength and TOA as well as using any recent location        center target MS location estimates.    -   (5d) The MBS 148 repeatedly provides the MBS operator with new        target MS location estimates provided using MS location        estimates provided by the MBS itself and by the location center        142 via the fixed location base station network.    -   (5e) The MBS 148 becomes the primary base station for the target        MS 140 and therefore controls at least the signal strength        output by the target MS.

Note, there can be more than one MBS 148 tracking or locating an MS 140.There can also be more than one target MS 140 to be tracked concurrentlyand each target MS being tracked may be stationary or moving.

MBS Subsystem Architecture

An MBS 148 uses MS signal characteristic data for locating the MS 140.The MBS 148 may use such signal characteristic data to facilitatedetermining whether a given signal from the MS is a “direct shot” or anmultipath signal. That is, in one embodiment, the MBS 148 attempts todetermine or detect whether an MS signal transmission is receiveddirectly, or whether the transmission has been reflected or deflected.For example, the MBS may determine whether the expected signal strength,and TOA agree in distance estimates for the MS signal transmissions.Note, other signal characteristics may also be used, if there aresufficient electronics and processing available to the MBS 148; i.e.,determining signal phase and/or polarity as other indications ofreceiving a “direct shot” from an MS 140.

In one embodiment, the MBS 148 (FIG. 11) includes an MBS controller 1533for controlling the location capabilities of the MBS 148. In particular,the MBS controller 1533 initiates and controls the MBS state changes asdescribed in FIG. 12. Additionally, the MBS controller 1533 alsocommunicates with the location controller 1535, wherein this lattercontroller controls MBS activities related to MBS location and target MSlocation. The location controller 1535 receives data input from an eventgenerator 1537 for generating event records to be provided to thelocation controller 1535. For example, records may be generated fromdata input received from: (a) the vehicle movement detector 1539indicating that the MBS 148 has moved at least a predetermined amountand/or has changed direction by at least a predetermined angle, or (b)the MBS signal processing subsystem 1541 indicating that the additionalsignal measurement data has been received from either the locationcenter 142 or the target MS 140. Note that the MBS signal processingsubsystem 1541, in one embodiment, is similar to the signal processingsubsystem 1220 of the location center 142. may have multiple commandschedulers. In particular, a scheduler 1528 for commands related tocommunicating with the location center 142, a scheduler 1530 forcommands related to GPS communication (via GPS receiver 1531), ascheduler 1529 for commands related to the frequency and granularity ofthe reporting of MBS changes in direction and/or position via the MBSdead reckoning subsystem 1527 (note that this scheduler is potentiallyoptional and that such commands may be provided directly to thedeadreckoning estimator 1544), and a scheduler 1532 for communicatingwith the target MS(s) 140 being located. Further, it is assumed thatthere is sufficient hardware and/or software to appear to performcommands in different schedulers substantially concurrently.

In order to display an MBS computed location of a target MS 140, alocation of the MBS must be known or determined. Accordingly, each MBS148 has a plurality of MBS location estimators (or hereinafter alsosimply referred to as location estimators) for determining the locationof the MBS. Each such location estimator computes MBS locationinformation such as MBS location estimates, changes to MBS locationestimates, or, an MBS location estimator may be an interface forbuffering and/or translating a previously computed MBS location estimateinto an appropriate format. In particular, the MBS location module 1536,which determines the location of the MBS, may include the following MBSlocation estimators 1540 (also denoted baseline location estimators):

-   -   (a) a GPS location estimator 1540 a (not individually shown) for        computing an MBS location estimate using GPS signals,    -   (b) a location center location estimator 1540 b (not        individually shown) for buffering and/or translating an MBS        estimate received from the location center 142,    -   (c) an MBS operator location estimator 1540 c (not individually        shown) for buffering and/or translating manual MBS location        entries received from an MBS location operator, and    -   (d) in some MBS embodiments, an LBS location estimator 1540 d        (not individually shown) for the activating and deactivating of        LBS's 152. Note that, in high multipath areas and/or stationary        base station marginal coverage areas, such low cost location        base stations 152 (LBS) may be provided whose locations are        fixed and accurately predetermined and whose signals are        substantially only receivable within a relatively small range        (e.g., 2000 feet), the range potentially being variable. Thus,        by communicating with the LBS's 152 directly, the MBS 148 may be        able to quickly use the location information relating to the        location base stations for determining its location by using        signal characteristics obtained from the LBSs 152.        Note that each of the MBS baseline location estimators 1540,        such as those above, provide an actual MBS location rather than,        for example, a change in an MBS location. Further note that it        is an aspect of the present disclosure that additional MBS        baseline location estimators 1540 may be easily integrated into        the MBS location subsystem 1508 as such baseline location        estimators become available. For example, a baseline location        estimator that receives MBS location estimates from reflective        codes provided, for example, on streets or street signs can be        straightforwardly incorporated into the MBS location subsystem        1508.

Additionally, note that a plurality of MBS location technologies andtheir corresponding MBS location estimators are utilized due to the factthat there is currently no single location technology available that isboth sufficiently fast, accurate and accessible in substantially allterrains to meet the location needs of an MBS 148. For example, in manyterrains GPS technologies may be sufficiently accurate; however, GPStechnologies: (a) may require a relatively long time to provide aninitial location estimate (e.g., greater than 2 minutes); (b) when GPScommunication is disturbed, it may require an equally long time toprovide a new location estimate; (c) clouds, buildings and/or mountainscan prevent location estimates from being obtained; (d) in some casessignal reflections can substantially skew a location estimate. Asanother example, an MBS 148 may be able to use triangulation ortrilateralization technologies to obtain a location estimate; however,this assumes that there is sufficient (fixed location) infrastructure BScoverage in the area the MBS is located. Further, it is well known thatthe multipath phenomenon can substantially distort such locationestimates. Thus, for an MBS 148 to be highly effective in variedterrains, an MBS is provided with a plurality of location technologies,each supplying an MBS location estimate.

In fact, much of the architecture of the location engine 139 could beincorporated into an MBS 148. For example, in some embodiments of theMBS 148, the following FOMs 1224 may have similar location modelsincorporated into the MBS:

-   -   (a) a variation of the TCSO FOM 1224 wherein TOA signals from        communicating fixed location BS's are received (via the MBS        transceiver 1512) by the MBS and used for providing a location        estimate;    -   (b) a variation of the artificial neural net based FOMs 1224 (or        more generally a location learning or a classification model)        may be used to provide MBS location estimates via, for example,        learned associations between fixed location BS signal        characteristics and geographic locations;    -   (c) an LBS location FOM 1224 for providing an MBS with the        ability to activate and deactivate LBS's to provide (positive)        MBS location estimates as well as negative MBS location regions        (i.e., regions where the MBS is unlikely to be since one or more        LBS's are not detected by the MBS transceiver);    -   (d) one or more MBS location reasoning agents and/or a location        estimate heuristic agents for resolving MBS location estimate        conflicts and providing greater MBS location estimate accuracy.        For example, modules similar to the analytical reasoner module        1416 and the historical location reasoner module 1424.

However, for those MBS location models requiring communication with thebase station infrastructure, an alternative embodiment is to rely on thelocation center 142 to perform the computations for at least some ofthese MBS FOM models. That is, since each of the MBS location modelsmentioned immediately above require communication with the network offixed location BS's 122 (152), it may be advantageous to transmit MBSlocation estimating data to the location center 142 as if the MBS wereanother MS 140 for the location center to locate, and thereby rely onthe location estimation capabilities at the location center rather thanduplicate such models in the MBS 148. The advantages of this approachare that:

-   -   (a) an MBS is likely to be able to use less expensive processing        power and software than that of the location center;    -   (b) an MBS is likely to require substantially less memory,        particularly for data bases, than that of the location center.

As will be discussed further below, in one embodiment of the MBS 148,there are confidence values assigned to the locations output by thevarious location estimators 1540. Thus, the confidence for a manualentry of location data by an MBS operator may be rated the highest andfollowed by the confidence for (any) GPS location data, followed by theconfidence for (any) location center location 142 estimates, followed bythe confidence for (any) location estimates using signal characteristicdata from LBSs. However, such prioritization may vary depending on, forinstance, the radio coverage area 120. In an one embodiment of thepresent disclosure, it is an aspect of the present disclosure that forMBS location data received from the GPS and location center, theirconfidences may vary according to the area in which the MBS 148 resides.That is, if it is known that for a given area, there is a reasonableprobability that a GPS signal may suffer multipath distortions and thatthe location center has in the past provided reliable locationestimates, then the confidences for these two location sources may bereversed.

In one embodiment of the present disclosure, MBS operators may berequested to occasionally manually enter the location of the MBS 148when the MBS is stationary for determining and/or calibrating theaccuracy of various MBS location estimators.

There is an additional important source of location information for theMBS 148 that is incorporated into an MBS vehicle (such as a policevehicle) that has no comparable functionality in the network of fixedlocation BS's. That is, the MBS 148 may use deadreckoning informationprovided by a deadreckoning MBS location estimator 1544 whereby the MBSmay obtain MBS deadreckoning location change estimates. Accordingly, thedeadreckoning MBS location estimator 1544 may use, for example, anon-board gyroscope 1550, a wheel rotation measurement device (e.g.,odometer) 1554, and optionally an accelerometer (not shown). Thus, sucha deadreckoning MBS location estimator 1544 periodically provides atleast MBS distance and directional data related to MBS movements from amost recent MBS location estimate. More precisely, in the absence of anyother new MBS location information, the deadreckoning MBS locationestimator 1544 outputs a series of measurements, wherein each suchmeasurement is an estimated change (or delta) in the position of the MBS148 between a request input timestamp and a closest time prior to thetimestamp, wherein a previous deadreckoning terminated. Thus, eachdeadreckoning location change estimate includes the following fields:

-   -   (a) an “earliest timestamp” field for designating the start time        when the deadreckoning location change estimate commences        measuring a change in the location of the MBS;    -   (b) a “latest timestamp” field for designating the end time when        the deadreckoning location change estimate stops measuring a        change in the location of the MBS; and    -   (c) an MBS location change vector.        That is, the “latest timestamp” is the timestamp input with a        request for deadreckoning location data, and the “earliest        timestamp” is the timestamp of the closest time, T, prior to the        latest timestamp, wherein a previous deadreckoning output has        its a timestamp at a time equal to T.

Further, the frequency of such measurements provided by thedeadreckoning subsystem 1527 may be adaptively provided depending on thevelocity of the MBS 148 and/or the elapsed time since the most recentMBS location update. Accordingly, the architecture of at least someembodiments of the MBS location subsystem 1508 must be such that it canutilize such deadreckoning information for estimating the location ofthe MBS 148.

In one embodiment of the MBS location subsystem 1508 described infurther detail hereinbelow, the outputs from the deadreckoning MBSlocation estimator 1544 are used to synchronize MBS location estimatesfrom different MBS baseline location estimators. That is, since such adeadreckoning output may be requested for substantially any time fromthe deadreckoning MBS location estimator, such an output can berequested for substantially the same point in time as the occurrence ofthe signals from which a new MBS baseline location estimate is derived.Accordingly, such a deadreckoning output can be used to update other MBSlocation estimates not using the new MBS baseline location estimate.

It is assumed that the error with dead reckoning increases withdeadreckoning distance. Accordingly, it is an aspect of the embodimentof the MBS location subsystem 1508 that when incrementally updating thelocation of the MBS 148 using deadreckoning and applying deadreckoninglocation change estimates to a “most likely area” in which the MBS 148is believed to be, this area is incrementally enlarged as well asshifted. The enlargement of the area is used to account for theinaccuracy in the deadreckoning capability. Note, however, that thedeadreckoning MBS location estimator is periodically reset so that theerror accumulation in its outputs can be decreased. In particular, suchresetting occurs when there is a high probability that the location ofthe MBS is known. For example, the deadreckoning MBS location estimatormay be reset when an MBS operator manually enters an MBS location orverifies an MBS location, or a computed MBS location has sufficientlyhigh confidence.

Thus, due to the MBS 148 having less accurate location information (bothabout itself and a target MS 140), and further that deadreckoninginformation must be utilized in maintaining MBS location estimates, afirst embodiment of the MBS location subsystem architecture is somewhatdifferent from the location engine 139 architecture. That is, thearchitecture of this first embodiment is simpler than that of thearchitecture of the location engine 139. However, it important to notethat, at a high level, the architecture of the location engine 139 mayalso be applied for providing a second embodiment of the MBS locationsubsystem 1508, as one skilled in the art will appreciate afterreflecting on the architectures and processing provided at an MBS 148.For example, an MBS location subsystem 1508 architecture may be providedthat has one or more first order models 1224 whose output is suppliedto, for example, a blackboard or expert system for resolving MBSlocation estimate conflicts, such an architecture being analogous to oneembodiment of the location engine 139 architecture.

Furthermore, it is also an important aspect of the present disclosurethat, at a high level, the MBS location subsystem architecture may alsobe applied as an alternative architecture for the location engine 139.For example, in one embodiment of the location engine 139, each of thefirst order models 1224 may provide its MS location hypothesis outputsto a corresponding “location track,” analogous to the MBS locationtracks described hereinbelow, and subsequently, a most likely MS currentlocation estimate may be developed in a “current location track” (alsodescribed hereinbelow) using the most recent location estimates in otherlocation tracks. Thus, the location estimating models of the locationcenter 139 and those of the MBS 148 are may be interchanged depending onthe where it is deemed most appropriate for such each such model toreside. Additionally, note that in different embodiments of the presentdisclosure, various combinations of the location center locationarchitecture and the mobile station architecture may be utilized ateither the location center or the MBS 148. Thus, by providingsubstantially all location estimating computational models at thelocation center 142, the models described here for locating the MBS 148(and equivalently, its incorporated MS 140) can be used for locatingother MSs 140 that are be capable of supporting transmission of wirelesssignal measurements that relate to models requiring the additionalelectronics available at the MBS 140 (e.g., GPS or other satellitesignals used for location).

Further, note that the ideas and methods discussed here relating to MBSlocation estimators 1540 and MBS location tracks, and, the relatedprograms hereinbelow are sufficiently general so that these ideas andmethods may be applied in a number of contexts related to determiningthe location of a device capable of movement and wherein the location ofthe device must be maintained in real time. For example, the presentideas and methods may be used by a robot in a very cluttered environment(e.g., a warehouse), wherein the robot has access: (a) to a plurality of“robot location estimators” that may provide the robot with sporadiclocation information, and (b) to a deadreckoning location estimator.

Each MBS 148, additionally, has a location display (denoted the MBSoperator visual user interface 1558 in FIG. 11) where area maps that maybe displayed together with location data. In particular, MS locationdata may be displayed on this display as a nested collection of areas,each smaller nested area being the most likely area within (any)encompassing area for locating a target MS 140. Note that the MBScontroller algorithm below may be adapted to receive location center 142data for displaying the locations of other MBSs 148 as well as targetMSs 140.

Further, the MBS 148 may constrain any location estimates to streets ona street map using the MBS location snap to street module 1562. Forexample, an estimated MBS location not on a street may be “snapped to” anearest street location. Note that a nearest street location determinermay use “normal” orientations of vehicles on streets as a constraint onthe nearest street location. Particularly, if an MBS 148 is moving attypical rates of speed and acceleration, and without abrupt changesdirection. For example, if the deadreckoning MBS location estimator 1544indicates that the MBS 148 is moving in a northerly direction, then thestreet snapped to should be a north-south running street. Moreover, theMBS location snap to street module 1562 may also be used to enhancetarget MS location estimates when, for example, it is known or suspectedthat the target MS 140 is in a vehicle and the vehicle is moving attypical rates of speed. Furthermore, the snap to street location module1562 may also be used in enhancing the location of a target MS 140 byeither the MBS 148 or by the location engine 139. In particular, thelocation estimator 1344 or an additional module between the locationestimator 1344 and the output gateway 1356 may utilize an embodiment ofthe snap to street location module 1562 to enhance the accuracy oftarget MS 140 location estimates that are known to be in vehicles. Notethat this may be especially useful in locating stolen vehicles that haveembedded wireless location transceivers (MSs 140), wherein appropriatewireless signal measurements can be provided to the location center 142.

MBS Data Structure Remarks

Assuming the existence of at least some of the location estimators 1540that were mentioned above, the discussion here refers substantially tothe data structures and their organization as illustrated in FIG. 13.

The location estimates (or hypotheses) for an MBS 148 determining itsown location each have an error or range estimate associated with theMBS location estimate. That is, each such MBS location estimate includesa “most likely MBS point location” within a “most likely area”. The“most likely MBS point location” is assumed herein to be the centroid ofthe “most likely area.” In one embodiment of the MBS location subsystem1508, a nested series of “most likely areas” may be provided about amost likely MBS point location. However, to simplify the discussionherein each MBS location estimate is assumed to have a single “mostlikely area”. One skilled in the art will understand how to provide suchnested “most likely areas” from the description herein. Additionally, itis assumed that such “most likely areas” are not grossly oblong; i.e.,area cross sectioning lines through the centroid of the area do not havelarge differences in their lengths. For example, for any such “mostlikely area”, A, no two such cross sectioning lines of A through thecentroid thereof may have lengths that vary by more than a factor offive.

Each MBS location estimate also has a confidence associated therewithproviding a measurement of the perceived accuracy of the MBS being inthe “most likely area” of the location estimate.

A (MBS) “location track” is an data structure (or object) having a queueof a predetermined length for maintaining a temporal (timestamp)ordering of “location track entries” such as the location track entries1770 a, 1770 b, 1774 a, 1774 b, 1778 a, 1778 b, 1782 a, 1782 b, and 1786a (FIG. 13), wherein each such MBS location track entry is an estimateof the location of the MBS at a particular corresponding time.

There is an MBS location track for storing MBS location entries obtainedfrom MBS location estimation information from each of the MBS baselinelocation estimators described above (i.e., a GPS location track 1750 forstoring MBS location estimations obtained from the GPS locationestimator 1540, a location center location track 1754 for storing MBSlocation estimations obtained from the location estimator 1540 derivingits MBS location estimates from the location center 142, an LBS locationtrack 1758 for storing MBS location estimations obtained from thelocation estimator 1540 deriving its MBS location estimates from basestations 122 and/or 152, and a manual location track 1762 for MBSoperator entered MBS locations). Additionally, there is one furtherlocation track, denoted the “current location track” 1766 whose locationtrack entries may be derived from the entries in the other locationtracks (described further hereinbelow). Further, for each locationtrack, there is a location track head that is the head of the queue forthe location track. The location track head is the most recent (andpresumably the most accurate) MBS location estimate residing in thelocation track. Thus, for the GPS location track 1750 has location trackhead 1770; the location center location track 1754 has location trackhead 1774; the LBS location track 1758 has location track head 1778; themanual location track 1762 has location track head 1782; and the currentlocation track 1766 has location track head 1786. Additionally, fornotational convenience, for each location track, the time series ofprevious MBS location estimations (i.e., location track entries) in thelocation track will herein be denoted the “path for the location track.”Such paths are typically the length of the location track queuecontaining the path. Note that the length of each such queue may bedetermined using at least the following considerations:

-   -   (i) In certain circumstances (described hereinbelow), the        location track entries are removed from the head of the location        track queues so that location adjustments may be made. In such a        case, it may be advantageous for the length of such queues to be        greater than the number of entries that are expected to be        removed;    -   (ii) In determining an MBS location estimate, it may be        desirable in some embodiments to provide new location estimates        based on paths associated with previous MBS location estimates        provided in the corresponding location track queue.        Also note that it is within the scope of the present disclosure        that the location track queue lengths may be a length of one.

Regarding location track entries, each location track entry includes:

-   -   (a) a “derived location estimate” for the MBS that is derived        using at least one of:        -   (i) at least a most recent previous output from an MBS            baseline location estimator 1540 (i.e., the output being an            MBS location estimate);        -   (ii) deadreckoning output information from the deadreckoning            subsystem 1527. Further note that each output from an MBS            location estimator has a “type” field that is used for            identifying the MBS location estimator of the output.    -   (b) an “earliest timestamp” providing the time/date when the        earliest MBS location information upon which the derived        location estimate for the MBS depends. Note this will typically        be the timestamp of the earliest MBS location estimate (from an        MBS baseline location estimator) that supplied MBS location        information used in deriving the derived location estimate for        the MBS 148.    -   (c) a “latest timestamp” providing the time/date when the latest        MBS location information upon which the derived location        estimate for the MBS depends. Note that earliest        timestamp=latest timestamp only for so called “baseline entries”        as defined hereinbelow. Further note that this attribute is the        one used for maintaining the “temporal (timestamp) ordering” of        location track entries.    -   (d) A “deadreckoning distance” indicating the total distance        (e.g., wheel turns or odometer difference) since the most        recently previous baseline entry for the corresponding MBS        location estimator for the location track to which the location        track entry is assigned.

For each MBS location track, there are two categories of MBS locationtrack entries that may be inserted into a MBS location track:

-   -   (a) “baseline” entries, wherein each such baseline entry        includes (depending on the location track) a location estimate        for the MBS 148 derived from: (i) a most recent previous output        either from a corresponding MBS baseline location estimator,        or (ii) from the baseline entries of other location tracks (this        latter case being the for the “current” location track);    -   (b) “extrapolation” entries, wherein each such entry includes an        MBS location estimate that has been extrapolated from the (most        recent) location track head for the location track (i.e., based        on the track head whose “latest timestamp” immediately precedes        the latest timestamp of the extrapolation entry). Each such        extrapolation entry is computed by using data from a related        deadreckoning location change estimate output from the        deadreckoning MBS location estimator 1544. Each such        deadreckoning location change estimate includes measurements        related to changes or deltas in the location of the MBS 148.        More precisely, for each location track, each extrapolation        entry is determined using: (i) a baseline entry, and (ii) a set        of one or more (i.e., all later occurring) deadreckoning        location change estimates in increasing “latest timestamp”        order. Note that for notational convenience this set of one or        more deadreckoning location change estimates will be denoted the        “deadreckoning location change estimate set” associated with the        extrapolation entry resulting from this set.    -   (c) Note that for each location track head, it is either a        baseline entry or an extrapolation entry. Further, for each        extrapolation entry, there is a most recent baseline entry, B,        that is earlier than the extrapolation entry and it is this B        from which the extrapolation entry was extrapolated. This        earlier baseline entry, B, is hereinafter denoted the “baseline        entry associated with the extrapolation entry.” More generally,        for each location track entry, T, there is a most recent        previous baseline entry, B, associated with T, wherein if T is        an extrapolation entry, then B is as defined above, else if T is        a baseline entry itself, then T=B. Accordingly, note that for        each extrapolation entry that is the head of a location track,        there is a most recent baseline entry associated with the        extrapolation entry.        Further, there are two categories of location tracks:    -   (a) “baseline location tracks,” each having baseline entries        exclusively from a single predetermined MBS baseline location        estimator; and    -   (b) a “current” MBS location track having entries that are        computed or determined as “most likely” MBS location estimates        from entries in the other MBS location tracks.        MBS Location Estimating Strategy

In order to be able to properly compare the track heads to determine themost likely MBS location estimate it is an aspect of the presentdisclosure that the track heads of all location tracks include MBSlocation estimates that are for substantially the same (latest)timestamp. However, the MBS location information from each MBS baselinelocation estimator is inherently substantially unpredictable andunsynchronized. In fact, the only MBS location information that may beconsidered predicable and controllable is the deadreckoning locationchange estimates from the deadreckoning MBS location estimator 1544 inthat these estimates may reliably be obtained whenever there is a queryfrom the location controller 1535 for the most recent estimate in thechange of the location for the MBS 148. Consequently (referring to FIG.13), synchronization records 1790 (having at least a 1790 b portion, andin some cases also having a 1790 a portion) may be provided for updatingeach location track with a new MBS location estimate as a new trackhead. In particular, each synchronization record includes adeadreckoning location change estimate to be used in updating all but atmost one of the location track heads with a new MBS location estimate byusing a deadreckoning location change estimate in conjunction with eachMBS location estimate from an MBS baseline location estimator, thelocation track heads may be synchronized according to timestamp. Moreprecisely, for each MBS location estimate, E, from an MBS baselinelocation estimator, there is substantially simultaneously a query of thedeadreckoning MBS location estimator for a corresponding most recentchange in the location of the MBS 148. Accordingly, E and the retrievedMBS deadreckoning location change estimate, C, have substantially thesame “latest timestamp”. Thus, the location estimate E may be used tocreate a new baseline track head for the location track having thecorresponding type for E, and C may be used to create a correspondingextrapolation entry as the head of each of the other location tracks.Accordingly, since for each MBS location estimate, E, there is a MBSdeadreckoning location change estimate, C, having substantially the same“latest timestamp”, E and C will be hereinafter referred as “paired.”

High Level Description of a Wireless Platform

FIG. 20 is a high level block diagram illustrating the wirelessapplication platform 2004 of the present disclosure in combination withvarious services and network components with which the platformcommunicates. In particular, the embodiment of FIG. 20 is illustrativeof how the platform 2004 communicates with, e.g., the subscribers (e.g.,users 2008), applications (e.g., applications 2016, 2020, 2024, 2028,and 2032 which may or may not receive wireless location relatedinformation from the wireless location gateway 142), and networkaccessible components (e.g., wireless equipment) for a single commercialwireless carrier. The platform 2004 communicates with subscribers orusers 2008 of the wireless carrier via, e.g., a mobile station 140 incommunication with various provisioning equipment and communicationservices of the wireless carrier, collectively this equipment andcommunication services are identified as carrier network provisioning2012, and may include e.g.:

-   -   1. wireless voice and/or wireless data (local and/or long        distance) services;    -   2. Internet access;    -   3. high speed data and/or Internet services such as (3G, cable,        DSL, ISDN, satellite communications, etc.);    -   4. telephony specific services (e.g., call forwarding, call back        busy, Caller ID, Do Not Disturb, prepaid calling card services,        etc.);    -   5. PBX and/or business network installation and maintenance        services;    -   6. teleconferencing provisioning and services; and/or    -   7. short messaging services (SMS).        More particularly, users 2008 can communicate various requests        to the platform 2004 for various wireless location related        services such as:    -   PR 1. Requests for routing the user from his/her location to a        desired location;    -   PR 2. Requests for information about products, services, places        and/or persons that are geographically related to a location of        the user 2008;    -   PR 3. Requests for displaying and/or modifying, e.g., user        profile information to thereby change access permissions, and/or        profile visibility;    -   PR 4. Requests for activating or deactivating services, e.g.,        wireless services such as hotel concierge wireless location and        routing services offered by hotel, such services capable of,        e.g., being attached and detached from a user's profile as a        unit;    -   PR 5. Requests for procuring products and/or services (location        related or otherwise); and/or    -   PR 6. Standard telephony, Internet and data services.

It is worth noting that embodiments of related wireless platforms havebeen described in the art. In particular, International PatentApplication PCT/US01/02526, filed Jan. 26, 2001 by McDowell et. al.titled: “Method and Apparatus For Sharing Mobile User Event InformationBetween Wireless Between Wireless and Fixed IP Networks” incorporatedherein fully by reference, and, International Patent ApplicationPCT/US02/04533, filed Feb. 15, 2002 by McDowell et. al. titled: “Use OfPresence And Location Information Concerning Wireless Subscribers ForInstant Messaging And Mobile Commerce” also incorporated herein fully byreference. However, these platforms appear directed to short messagingservice applications and ecommerce (i.e., merchant advertising), and donot appear to address issues related to the easy incorporation ofentirely new complex network services, and in particular, networkservices wherein there is a uniform architecture for communicationsbetween the platform and new network service applications. Instead, thePCT/US02/04533 application is directed to: “the integration of presencedetermination, location determination, Instant Messaging, and mobilecommerce into a functionally seamless system” wherein such presencedetermination “determines whether a mobile device is ON or OFF inreal-time.” So that this system (i.e., McDowell's) “may then share therevenue generated through the sale of subscriber information with theparticipating wireless carriers that host the subscribers.”, and“determines both Internet presence and wireless network presence, andmakes this information available to entities on both networks.” However,the above-identified McDowell et. al. PCT patent applications do provideappropriate supportive and enabling information for the presentdisclosure, and in particular, the platform 2004.

FIG. 22 shows an embodiment of the high level steps performed that canbe performed by the platform 2004. Descriptions of these steps follows:

-   -   Step 2204: The subscriber interfaces 2104 (FIG. 21) receives a        service request from a user 2008, via the carrier network        provisioning 2012 (FIG. 20). Note that such service requests may        be from users 2008 where such users include not only persons,        but also entities such as businesses, employers, other        telecommunication carriers, government agencies (e.g., command,        control, and communications centers), law enforcement, etc. In        at least some circumstances, the actual payload of the data        describing the service request and/or related data in the        request may be encrypted. Thus, the present step determines        whether one or more portions of the service request is        encrypted, and if so, activates the encryption and decryption        component 2108 (FIG. 21) for decrypting the service request.        Encryption/decryption cyphers are well known in the art, and        accordingly will not be discussed at length here. However, the        encryption and decryption component 2108 may support a        substantial number encryption/decryption cyphers, (e.g., RC4 and        RSA, by Security Inc, Belford, Mass., USA) as well as such        general encryption techniques as public/private key        cryptographic technique such as Diffie-Hellman.        -   -   Note that the present step may identify, e.g., at least                some of the following data items:

        -   (i) the identity of the requester;

        -   (ii) the identity of an entity (or entities) to whom an            action of the request is directed, e.g., (a) the identity of            the person or MS 140 whose wireless location is requested            (this may be the mobile identification number (MIN) as one            skilled in the art will understand), or (b) the identity of            a package whose whereabouts is being tracked, (c) the            location of an MS 140 which to be identified (e.g., in a            battlefield context to determine if the location of the MS            corresponds to friend or foe);

        -   (iii) any additional data that may be needed by an            application activated to fulfill the request, e.g., for an            MS 140 location request, this may include the last known            location of the MS;

        -   (iv) any timing constraints that the service requesting            application should aware of;

        -   (v) any authorization code needed for granting access to any            generated information about the entity (e.g., for            determining a subscriber's location, a code indicating that            permission has been obtained to locate the subscriber, or a            code indicating that location of the subscriber is at the            request of the government agency responsible for national            security or crime prevention);

        -   (vi) any encryption parameters needed for a resulting            response to the request;

        -   (vii) the identity of any specific application to be            activated to fulfill the request;

        -   (viii) any billing code required in order to bill for            fulfilling the service request;

        -   (ix) a priority for fulfilling the service request (note,            emergency 911 and other time critical life threatening or            emergency services will have highest priority and may            pre-empt other service requests being processed by the            platform 2004;

        -   (x) identity of all destinations, entities and/or persons to            which the results from the fulfillment (and/or activation)            of the service request is to be transmitted;

        -   (xi) any authorization code or protocol to be used in            identifying the appropriate person or entity prior to            presenting information related to the results of the service            request.            -    Further note, however, that it is not intended that the                user 2008 be required to enter all of the items                identified in this step. In particular, many of these                items may be automatically filled in with default values                residing on the user's service requesting device.    -   Step 2208: With any decryption completed, the service request is        now readable and accordingly may be logged in the user request &        response log management database 2112 (FIG. 21) so that,        e.g., (i) audits can be performed for verifying what service        requests have been received, (ii) analyzing platform 2004        performance, diagnosing errors in service request processing,        and/or statistical analysis of service request volume may be        performed, and (iii) tracking or identifying criminal behavior        and/or misuse of a service offered by the platform 2004.    -   Regarding the request & response log management database 2112,        this database may capture and store at least most of the        following information related to a service request received by        the platform 2004:        -   (a) The identity of the party initiating the service            request, e.g., a user ID or log in name;        -   (b) The time of receipt of the service request;        -   (c) The identity of the service requested;        -   (d) The priority of the service request (if any provided);        -   (e) Any time constraints that the service request is            imposing (e.g., a response within 30 seconds);        -   (f) Information related to the source of the request, e.g.,            the MIN (or other identification) of an MS 140 requesting            service, or an Internet address of a service requester;        -   (g) Any authorization code for permitting the service            request to be performed; and        -   (h) Any billing code identifying who is to be charged.    -   Step 2212: Subsequently, a readable version of the service        request is provided to the subscriber identification &        application authorization subsystem 2116 (FIG. 21), wherein the        identification of both the requester and the application to be        activated to fulfill the service request is determined. The        subsystem 2116 may access various user identification        repositories, such as user profile repositories collectively        labeled 2120 (FIG. 21), including customer care data management        systems that are maintained by, e.g., a wireless carrier        responsible for the operation of the platform 2004, such        repositories being, e.g., home location registers (HLRs) and        Visitor Location Registers (VLRs). Additionally, some of the        repositories 2120 may be accessed only via another network        carrier not affiliated or responsible for the operation of the        platform 2004. Such repositories may be accessed for obtaining,        e.g., (i) additional user information that may not have been        provided with the service request, and/or (ii) an identification        of the carrier network (if any) to which the user is a        subscriber. In particular, such additional information may        relate to an authorization to activate, e.g., a wireless        location based application, and receive a response therefrom.        Note that such authorization may include two processes: a        determination of whether the user is eligible to make the        request (e.g., such eligibility may be substantially determined        according to, e.g., the service package to which the user 2008        has subscribed and whether the user's subscription remains        active), and a determination as to whether the current service        request can be honored given privacy, security, and/or legal        constraints that must satisfied for fulfilling the service        request, e.g., location based network services where a person        different from the user 2008 is to be located.    -   In one embodiment, if the user 2004 is a roamer (civilian or        military), the network carrier operably responsible for the        platform 2004 may initiate, via the subsystem 2116, a request        for user profile information to be transmitted from the user's        subscriber network or other central profile repository. Various        embodiments of such profiles and/or data within them are        provided throughout this description. Thus, a user profile may        include substantially any user information that is required to        allow or prohibit access, activation, or fulfillment of a        network service by the user, or, by another user where the        requested service, by the other user, requires accessing        information about the user that is identified as being        confidential or private. However, in one preferred embodiment        such user profiles may be automatically requested when the        roamer activates his/her MS 140 for out of network service.        Moreover, it may be the case that when fulfillment of the        service request requires the location or other personal        information (e.g., financial information) of another user or        entity, at least a portion of the profile for this other user or        entity must be queried or accessed for determining whether such        a location activity is permissible and/or legal, and such        information may be substantially only accessible from the        carrier network to which the user is a subscriber.    -   In order to identify the service being requested, the subsystem        2116 can access the user assessable & authorized services        database 2124 (FIG. 21) for determining the services that are        currently accessible from via the platform 2004, e.g., as called        services or platform aware connection services as described in        the Summary section hereinabove. Additionally, the database 2124        may be accessed by the subsystem 2116 for retrieving information        related to who is authorized to access certain services. For        example, certain network services may be available for only a        particular time period(s). For example, a particular network        based game may extend for a predetermined time period such as        three weeks, or may be only played on non-holiday weekends when        there is less network traffic. In such a case, it may more        expedient to associate game activation authorization data with        information identifying the game in the database 2124 than        iteratively modifying, e.g., user 2008 profiles of game players        for indicating when the game can be accessed as a network        service. Additionally, note that a network service that is        malfunctioning may be easily prevented from being accessed if        such authorizations are associated with network service        identifications. Furthermore, it may be the case, that an        alternative service provider may be utilized for fulfilling the        service. Thus, the preferred (now malfunctioning) service        provider may be effectively disconnected from being accessed by        users 2008, and a second less preferred backup network service        activated for the providing substantially the same service in a        manner that is transparent to the users 2008. Examples where        such backup service providers may be desirable are: (i) when        wireless location requests must be fulfilled (e.g., E911        requests) and the primary wireless location service provider is        experiencing operational difficulties, then a second less        desirable backup wireless location service provider may be        easily activated (assuming all communication and data flow paths        with the second location service provider have been previously        established) by merely changing the value of the activation        information for each of the primary and secondary wireless        location service providers in the database 2124, (ii) when a        service provider for an Internet service 2128 (FIG. 21) such as        a service provider for an Internet connection, or some other        Internet accessible service such as a search engine or a        battlefield command and control Internet site becomes        inoperative, then users 2004 may be transparently (or        substantially so) switched to a corresponding backup service        provider for the Internet service. Thus, the database 2124 may        allow for providing a simple and effective technique for        providing the platform 2004 with a measure of fail safeness to        network services that are accessible via the platform 2004.    -   Note that the services & applications 2016 (FIG. 20) are        representative examples of some of the services that may be        requested as called services. However, these services may also        be connection services, e.g., a 911 call may be a voice over IP        connection which also provides the FCC mandated information to        the 911 center. The services identified in 2016 will how be        briefly described:        -   i. Yellow page services related to the purchase of products            and/or services, and in particular electronic networked            yellow page services as described more fully under the            section Wireless Location Applications hereinbelow;        -   ii. Emergency services such as E911 in the USA (note that            emergency services are typically routed through            substantially dedicated channels; however, it is believed            that with increasing network bandwidth and robustness, such            dedicated channels can be substantially dispensed with and,            instead, such emergency services can be appropriately and            timely performed by using the platform 2004 of the present            disclosure. Moreover, by utilizing the platform 2004,            emergency services may be significantly enhanced by, e.g.,            accessing the emergency caller's profile and thereby            alerting friends, relatives, neighbors, and/or appropriate            passersby. Additionally, caller medical information may be            provided in the caller's profile such as type of medical            insurance, caller medical conditions, and/or medical            personal to be alerted;        -   iii. 411 information services, and in particular, location            based information services, and more particularly            “intelligent” location based information services such as            the location based routing services described hereinbelow in            the section titled Routing Applications, and the section            titled Point of Interest Applications hereinbelow;        -   iv. Roaming services such as wireless concierge services            that may offered to travelers by, e.g., hotels as described            more fully in the section titled Roaming Services            hereinbelow.    -   Note, however, that for different application domains very        different network services may be available. For example, in a        military or battlefield context there may be analogous services        to some of the items (i) through (iv) immediately above;        however, certainly additional network services are likely such        as network services for real time control over robotic or        surveillance battlefield devices.    -   Step 2216: Subsequently, a determination is made by the        subscriber identification & application authorization subsystem        2116 as to whether the network service request is an emergency        such as an E911 request.    -   Step 2220: If the results from Step 2216 is positive, then the        subsystem 2116 activates an emergency protocol for communicating        with one or more emergency response service providers 2132        (represented in FIG. 21 by the 911 processing block 2132),        whereby, e.g., a predetermined series of emergency tasks or        steps are performed for: (i) locating the emergency, (ii)        identifying the type of emergency, and (iii) directing        assistance to the emergency or directing persons out of the        emergency. When the platform 2004 is used for accessing network        services within a U.S. commercial mobile radio provider network        (CMRS), the U.S. Federal Communications Commission (FCC)        provides guidelines and mandates regarding how and what        emergency tasks are performed. Such emergency protocols are well        known in the art and are not elaborated on here. However, note        that such emergency protocols may be different when the platform        2004 is utilized in a military or battlefield context, or in the        context of a major disaster such as damage from a hurricane or a        biological terrorist attack in that there may be many requests        for emergency services within a relatively short timeframe        (e.g., 1 minute to 12 hours or longer). However, whether the        platform 2004 is utilized in a civilian or military context, a        high rate of emergency service requests can be problematic for        the communications network to appropriately handle. In one        embodiment, of the platform 2004, the subsystem 2116 detects        high rates of emergency requests, and alerts a platform        controller 2136 (FIG. 21) which, e.g., allocates computational        resources within the platform 2004, and handles error or        exceptional event processing. The controller 2136 may in one        embodiment, modify the database 2124 so that when the subsystem        2116 subsequently accesses this database for determining an        emergency response service provider to service emergency        requests, the database 2124 commences to distribute the output        identifications of emergency response service over a plurality        of such service providers. Thus, two successive requests for an        emergency response service provider by the subsystem 2116 may        result in different in identifications of two different service        providers, whereas without the controller 2136 database        modification, the same emergency response service provider would        have been provided to the subsystem 2116. Note that the database        2124 may use a static or fixed allocation scheme for allocating        emergency service requests among a plurality of emergency        response service providers 2132 operatively connected to the        platform 2004. Alternatively, a dynamic scheme may be used        wherein there is feedback to the platform 2004 (and more        particularly, the controller 2136) from each (or at least some)        of the emergency response service providers 2132 providing data        indicative of the emergency processing loads they are        experiencing. For example, such feedback from an emergency        response service provider may include one or more of: (i) a        measurement related to the number of emergency requests that are        queued and not currently being processed (e.g., the current        number or the average over some time period); (ii) a measurement        related to the rate at which emergency requests are being        processed (e.g., an average number of emergency requests fully        processed in a particular time period); (iii) one or more        measurements related to the time to process a specified number        of emergency requests (e.g., an average time for fully        processing a moving window of 10 emergency requests, a        percentage of the number of emergency requests being currently        processed that are identified as likely to require very lengthy        or an indeterminate amount of time to process; (iv) a        measurement related to the overall emergency response processing        load (e.g., this measurement identified as high whenever a        measurement for (i) above exceeds a predetermined threshold, or        a measurement for (iii) above exceeds a predetermine threshold).    -   Thus, upon receiving such feedback, the controller 2136 may be        able to adjust the distribution of emergency requests among the        emergency response service providers to thereby balance the        loads on these service providers, or provide a higher emergency        response completion rate, or provide a lower average time for        providing an initial response to emergency requests.        Additionally, the controller 2136 may provide instructions to,        e.g., subscriber identification & application authorization        subsystem 2116 so that during such load balancing, geographic        location information of the user initiating emergency or 911        requests is used in the routing of such requests to particular        emergency service providers (e.g., public safety answering        points, or PSAPs). For example, a large hurricane may impact an        area having a radius of 100 miles or more. Accordingly, numerous        emergency service providers local to such an area may be        overwhelmed with emergency calls. Accordingly, one or more        emergency command centers may be setup to coordinate emergency        response services, wherein such emergency command centers        communicate with the emergency service providers (e.g., PSAPs)        in the emergency effected area. Accordingly, when load balancing        is initiated to direct some (or all) calls to alternative        emergency service providers (e.g., PSAPs) instead of such local        emergency service providers, at least emergency command center        communication information is also provided to the alternative        emergency service providers so that they can communicate with        the one or more emergency command centers for the geographic        areas from which such alternative emergency service providers        are receiving emergency requests. Thus, in one embodiment, when        load balancing between emergency service providers (e.g.,        PSAPs), the geographic location of each emergency request may be        determined and used for determining the alternative emergency        service provider to route the request. In particular, the        alternative emergency service provider should have        communications established with the corresponding emergency        command center responsible for the area from which the emergency        request is received. Accordingly, such load balancing may        additionally cause all calls which are to be re-routed from an        overloaded (or non-functioning) emergency service provider to        re-routed one or more predetermined or pre-selected alternative        emergency service providers. Thus, the following substeps may be        performed during load balancing for each emergency request        within an emergency or disaster area; i.e., if at least one load        balancing metric indicates load balancing between an emergency        service provider P and some alternative emergency service        provider Q_(i) is needed, then:        -   Determine a percentage (or other measurement) of the            emergency requests to be off-loaded from P to some Q_(i). In            one embodiment, such a percentage may be:        -    The maximum, less than or equal to 100 (100 otherwise), of:            -   [100*((terminated requests not processed by P within                time period T)/(emergency requests received by P within                T))+predetermined additional percentage (e.g., 5)], and            -   [100*(1+((emergency requests fulfilled by P within                T)/(emergency requests received by P within T))],        -    wherein the time period T may be, e.g., from one minute to            5 minutes, and wherein T may vary inversely with the number            (or change in number) of emergency call requests in the            immediately preceding one or more time periods (denoted here            the “call volume”). Thus, assuming a substantially inversely            linear relationship between T and the emergency call volume,            if the call volume rises from an average of 50 calls for the            time period sT of a preceding collection of one or more time            periods (e.g., having an average length of 3 minutes) to 100            calls in the current time period T_(C) (e.g., of say 3.5            minutes), then the next time period T_(N) may be between            1.75 and 1.5 minutes depending the number of preceding time            periods used.        -   For each emergency request to be re-routed to an alternative            emergency service provider Q_(i), select Q_(i) according to            whether Q_(i) is configured to communicate with the command            center C for the area containing the geographic location of            the source of the request, and wherein Q_(i) is the first            such configured alternative emergency service provider on a            list of alternative emergency service providers for P that            is not also requiring load balancing.        -   If no Q_(i), was selected in (b) above, notify emergency            command center C.    -   Additionally, such re-routing of emergency requests may also be        dependent upon a geographical location of the emergency caller.        In particular, emergency requests from predetermined        geographical subareas of a overloaded emergency service provider        may be routed to the same alternative emergency service        provider. Note, that such re-routing based on emergency caller        location can be performed by identifying (for wireless emergency        requests) the primary base station with which the caller is in        contact. Accordingly, it is believed that in most emergency or        disaster designated areas, emergency calls from subareas thereof        will be processed by at most one or two emergency service        providers (e.g., PSAPs) while at the same time balancing call        loads between emergency service providers.    -   Moreover, the present step also includes providing what is known        as “reverse 911” protocols, wherein persons in a given area are        alerted to an eminent or likely emergency situation or event        which may be dangerous to them, e.g., an impending flood, an        enemy aircraft that is nearby, a change in the direction of a        forest fire or hurricane, etc. Thus, for such reverse 911        service requests, the requestor is likely to be a governmental        agency or designated agent (e.g., a field observer), and        location information, e.g., indicating the area to likely be        affected by the imminent threat is provided with the service        request. Accordingly, subscribers (and others that can be        contacted) whose location is identified as being in designated        area are notified of the danger. Thus, it is an aspect of the        platform 2004 to push certain types of information to users' MSs        140 such as reverse 911 information.    -   Step 2224: If the result from step 2216 indicates that the        service request is not for an emergency, then in step 2224 the        subsystem 2116 may access a home location register for the user,        a visitor location register providing user information, wherein        such an access is for obtaining profile data for the user        related to the current request. That is, the present step        accesses the database(s) 2120 for retrieving profile information        for the user 2008 requesting the service, and/or the user        profile information related to the service or application being        requested.    -   Step 2228: In the present step a determination is made by the        subsystem 2116 as to whether the application being requested (or        a comparable application) is known to the platform 2004; e.g.,        registered with the platform so that platform can enable the        service and/or perform accounting for the service. Note that for        roaming MS 140 users, they may request one or more services that        are not available in a network in which they are roaming.        However, substantially or somewhat comparable services may be        available, and the platform 2004 may inform such a (roaming)        user of the comparable service for thereby obtaining user input        regarding whether to allocate resources for the comparable        service, or alternatively automatically activate the comparable        service for the user. For example, for a user whose medical        condition is monitored continuously or periodically via        automatic transmissions from the user's MS 140 to a medical        monitoring service, the user may roam to a geographical area        where the user's medical monitoring service is not provided. For        instance, a user may have his/her heart rhythms monitored by a        medical monitoring service. However, upon roaming to an area not        covered by the medical monitoring service or not covered (or not        covered efficiently due to time delays and/or cost), the user        may be: (i) informed of such lack of coverage, and (ii) allow        the user to request activation of an alternative service, (if        available) and/or the user may automatically be provided with        the alternative service such as a service that merely monitors        blood pressure. Other examples may be also instructive. A user        have contracted with a particular rental car agency to supply        the user with a particular type of car substantially wherever        the user travels, and the user has requested such a car for        his/her arrival in Santa Fe, N.Mex. However, upon arriving in        Santa Fe, N. Mex., the user may be notified via the platform        2004: (i) that the particular type of car is unavailable,        and (ii) what alternative car selections the rental agency has,        and/or other car rental agencies have the particular car that        the user can rent. In anther example, a user may be provided        with periodic information regarding another user's or object's        status, e.g., user/object's geographic location, the temperature        of the user/object, a movement of the user/object, a        configuration of user/object (e.g., computer network        configuration), etc. However, if the user/object moves to an        area that can not support (or cost effectively) can support the        transmission of such information, a comparable service (if        available) may be offered to the user.    -   Step 2232: If the result from step 2228 is negative, then in one        embodiment of the present step, an applications controller 2144        and more particularly, application access initialization 2148        attempts to obtain data for initializing access to the requested        (or comparable) service, and/or to provide the billing system        2140 with sufficient information for billing for the service        request. For example, the platform 2004 may request activation        schema or script data from the requested service for activating        the service for the user. Such schema or script data may include        identifications of network bandwidth required/desired, when to        activate the service, the length of time network resources will        be needed, network quality of transmission characteristics        required/desired, and/or payment information for billing the        user via the user's network provider. If the application access        initialization 2148 is successful in obtaining sufficient        information, then the retrieved application request description        data may be in the application requirements database management        system 2152. However, in another alternative embodiment of the        present step, the application access initialization 2148 outputs        a failure code for the request, and this code is provided to the        subscriber interfaces component 2104, wherein an appropriate        representation of this failure is presented to the user 2008 by        accessing the presentation engine 2156 for generating a        presentation that is presentable at the user's network device        such as an MS 140. Subsequently, in this alternative embodiment,        the process of FIG. 22 terminates relative to the service        request being processed.    -   Step 2236: If the result from step 2228 is positive or the        requested application can be otherwise initiated via step 2232,        then in one embodiment the subsystem 2116 determines whether        there is authorization for activating an application for        fulfilling the service request. In one embodiment, the billing        system 2140 (FIG. 21) may be accessed for determining whether        the request by a user 2008 should be honored. Note such access        to the billing system 2140 may be desirable since an important        aspect of the platform 2004 is the ability to provide common        network services (and in particular complex network services,        and more particularly, wireless location based network services)        to a large and potentially varying number of network services.        That is, it may be the case that a user 2008 is denied further        access to a particular network service by the platform 2004 due        to a delinquent payment or disputed charges, but the user is        given access to other network services (e.g., network services        paid by another, such as an employer, a parent, a financial        institution, etc.).    -   Step 2240: If the result of step 2236 is negative, then in a        similar manner to the alternative embodiment of step 2232 a        failure indication is output to the user.    -   Step 2244: If the result of step 2236 is positive, then the        applications controller 2144 performs the following steps: (a)        it parses the service request for identifying service request        specific data; (b) it prioritizes the service request according        to, e.g., desired performance requirements (e.g., network        bandwidth requirements, security requirements, encryption        requirements, response requirements, etc.) for fulfilling the        service request and priority; and (c) if needed, determines        network access paths for accessing the application that can        fulfill the service request, and/or activates the request        provisioning system 2160 for determining/allocating network        resources such as equipment and bandwidth (e.g., virtual private        communication channels or allocating bandwidth for a user        requested movie to be streamed to his/her MS 140).    -   Step 2248: In the present step, the applications controller 2144        in combination with the request provisioning system 2160: (a)        accesses the applications requirements data management system        2152 to determine what activations of other network services are        required by the current service request being processed by the        applications controller 2144, and (b) determines how such        additional network service output(s) is to be provided to the        current service request being processed; e.g., output format,        output timing restrictions, accuracy restrictions, etc. Note        that the applications requirements data management system 2152        may include scripts or other interpretative or executable code        that identifies a series of intermediate service requests that        must be performed to the fulfill the user's input service        request. Moreover, in some embodiments, the user's input service        request may substantially identify such intermediate steps and        thereby over ride any default intermediate service requests in        the data management system 2152. In particular, the user service        request input may be declarative in nature, wherein the user        input identifies what is to be performed in as much detail as        desired, and the system 2152 determines the mapping between a        desired output and the one or more service requests that need to        be fulfilled in order to fulfill the user's request. Thus, for        each service request for which the platform 2004 is responsible        for processing the request, the system 2152 includes, e.g., a        script, schema or other data structure indicating the services        to be activated, and any sequencing of those services. Note that        by providing such data structures (e.g., service request        scripts) so that they are accessible by the platform 2004, the        following advantages are obtained: (1) any backup or alternative        services that can be used may be performed as necessary without        the users 2004 having to specify such alternatives; (2) network        and/or service request enhancements may be more easily utilized        in fulfilling certain service requests; e.g., certain location        based service requests may require a particular location        accuracy and such accuracy may require activating more than one        location service provider. Typically, the wireless location        gateway or location center 142 would provide such functionality.        However, certain networks may assume such functionality, and not        utilize such a gateway, and the platform 2004 may assume such        responsibility. Accordingly, such scripts for location based        services that require a predetermined accuracy may be modified        without the need to change user service request inputs to the        platform 2004. Thus, a location based dating service may require        location based information of mobile stations 140 that are        within 20 meters of one another, and it may be determined (e.g.,        through user complaints) that the accuracy currently being        provided is insufficient. Thus, the corresponding script for        fulfilling an activation of the dating service request may be        changed to use additional location service providers and/or a        location gateway 142 entirely transparent to the users 2008. In        another example, if the platform 2004 offers a service to obtain        estimates for obtaining discounted hotel rooms for users 2008        seeking immediate occupancy in a relatively local geographical        area (e.g., a city or within 5 miles of the user), the script        for such a service may change frequently according to season,        occupancy rates, hotels opting in or out of such a service.    -   Step 2252: A determination is made by the applications        controller 2144 as to whether there are currently sufficient        network resources available to appropriately fulfill the service        request currently being processed (more precisely, attempting to        be processed).    -   Step 2256: If the result from step 2252 is negative, then in one        embodiment of the present step, the applications controller 2144        requeues the current service for examining at a later time and        commences processing another service request as the current        request.

Additionally, the applications controller 2144 may issue an allocationrequest to the request provisioning system 2160 to reserve certainnetwork resources (e.g., reserve a high bandwidth data channel) if suchis needed by the previous “current” service that has been requeued. Ifthe requeued service request is not processed within a request specificamount of time, then as in the alternative embodiment of step 2232, theuser 2008 is informed of the failure of the service request. However, inone alternative embodiment, instead of notifying the user 2008 offailure, the user may be notified that there is a delay in fulfillingthe service request, and the user may be provided with the option ofcanceling the service request or waiting for its fulfillment.

-   -   Step 2260: The applications controller 2144 activates one or        more applications for fulfilling the service request currently        being processed since all the network resources it requires are        available as well as the application(s) for fulfillment of the        request. For example, the applications illustrated in FIG. 20        may be activated by applications controller 2144, and such an        activation(s) may be via the application gateway (FIG. 21) for        activating specific applications as one of ordinary skill in the        art will understand. Note that the service request data        processed by the applications controller 2144 may be in the form        of a script that the controller 2144 interprets.    -   Step 2264: In some circumstances, service requests are        automatically activated as, e.g., intermediate steps in        fulfilling another service request. Accordingly, the present        step illustrates the performance of such automatically activated        service requests.

The above high level description of the processing performed by theplatform 2004 is also applicable to disaster or large scale emergencymanagement and communication such as occurs during hurricanes, floods,earthquakes, combat situations, and the like where communications andapplications may need to be modified rapidly. In particular, byproviding a standard script or schema for requesting network servicesand/or provisioning, network applications may be easily incorporatedinto embodiments of the present disclosure. For example, during ahurricane or earthquake, high call volume capacity mobile base stationsmay be required to compensate for damaged or malfunctioninggeographically fixed base stations of a wireless carrier'sinfrastructure, and/or to handle an increase in call volume. However,network services not related to disaster assistance may need to beseverely restricted, and such restrictions may be easily provided viathe platform 2004 since the applications requirements databasemanagement system 2152 (FIG. 21) to temporarily disable or restrictnon-essential network services (e.g., high bandwidth network games,etc.). Moreover, by establishing appropriate profiles for emergencyresponder personnel in the user profile repositories 2120, suchpersonnel may be able to activate network services/applications that maybe restricted from general use. So, e.g., while non-emergency requestsfor multi-point conference calls may be restricted or unavailable to thepublic in general, such network services may be available to variousemergency responder personnel. Thus, it is aspect of the presentdisclosure that authorization for accessing networkservices/applications can be selectively provided to one or more groupsof users, wherein individuals are identified as to whether they are suchgroups by accessing each individual's profile in the profilerepositories 2120. Accordingly, large numbers network users (where mostof the users are unrelated to one another at least in the sense that thenetwork services for which each has contracted is unrelated to thecontracted network services of most others) may be restricted fromaccessing certain network services, and/or allowed to access certainnetwork services dependent upon, e.g., which group of a hierarchy ofnetwork user groups with which each user is identified via his/herprofile. Moreover, it important to note that network service requestsmay by grouped into one or more groups, wherein access and/or denial ofnetwork services may depend on the user's geographic location (and/orthe location network infrastructure by which the user accesses thenetwork). For example, an MS 140 user within an area hit by a disastermay not be provided with wireless Internet service, whereas if this sameuser were in an area remote from the disaster wireless Internet servicemay be provided. Thus, in one embodiment, the following hierarchy ofnetwork service requests may be utilized (from highest priority fornetwork services to lowest priority for network services) when adisaster is declared, e.g., by a governmental entity:

-   -   (a) Emergency requests by personnel directors and supervisors        directing emergency assistance efforts related to the disaster        (such individuals having profiles for identifying themselves as        emergency personnel directors and supervisors). Note that such        directors and supervisors may be individuals in charge of        governmental and/or military operations related to the disaster;    -   (b) Emergency command center communications for directing        emergency assistance efforts related to the disaster (e.g., with        emergency responder personnel and/or emergency response centers,        e.g., PSAPs);    -   (c) Network emergency request calls by users dialing an        emergency number, e.g., 911; note that in one embodiment, the        locations of such users may be aggregated on a display device        for emergency response personnel (e.g., PSAP 911 operators) so        that emergency assistance personnel assisting one user can also        be directed to assist other nearby users also seeking emergency        assistance while such personnel reside in the area.    -   (d) Network service requests by users requesting non-emergency        network services within the disaster area (e.g., as defined by a        governmental entity). Accordingly, for each user within a        disaster area, each requested network access by the user may be        accompanied by information indicating the user is in the        disaster area (e.g., the identification of the primary base        station with which the user is communicating) so that the        request can be identified as having relatively low priority.        Additionally, a user outside of the disaster area that is        requesting access to network resources in the disaster area may        be prevented or delayed from doing so. For example, if the last        known wireless coverage area of a user being called is in the        disaster area (as, e.g., identified in the called user's home        location register or visitor location register), then the call        may be given a relatively low priority.

Thus, it is an aspect of the present disclosure that within a disasterarea, network allocation may be driven at least partially bygeographical location of network resources being requested.

Additional processing capabilities that various embodiments of theplatform 2004 will now be described:

-   -   (a) billing system 2140: Note that in one embodiment of the        platform 2004, the billing system 2140 (or an enhancement        thereto) is the billing system of the wireless carrier with whom        the user 2008 subscribes for wireless services. It is        contemplated that for various wireless applications, and        particularly location based applications, such applications can        be more quickly made available to subscribers 2008 if the        already existing network infrastructure and support services        (such as billing) are used. Thus, assuming an appropriate and        preferably uniform interface between service request fulfillment        application management processes (not shown) and the billing        system 2140 is provided, business rules, charges for existing,        new and removed application services maybe communicated to the        billing system 2140. Furthermore, such a central billing system        2140 makes it easier for network services, and in particular,        complex network services such as location based services to be        bundled or packaged together and potentially provided under the        trademarks or servicemarks of the wireless carrier even though        such “private label” applications (identified in FIG. 20 by the        components labeled 2020 and 2024) are owned and operated by        third parties. Moreover, such a central billing system 2140 also        has the advantage of providing fewer individual bills to the        subscribers 2008 in that charges for such services may be        incorporated into the bill provided by the subscriber's wireless        carrier;    -   (b) data exposure engine (FIG. 21): This component provides the        functionality described in the Wireless Application Platform        Services and Architecture section of the Summary entitled “data        exposure processing”.        Wireless Location Applications

Such wireless location applications as were briefly described above inreference to the gateway 142 will now be described in further detail.Note that the following location related services are considered withinthe scope of the disclosure, and such services can, in general, beprovided without use of a gateway 142, albeit, e.g., in a likely morerestricted context wherein not all available wireless locationestimating techniques are utilized, and/or by multiplying the number ofinterfaces to geolocation service providers (e.g., distinct wirelesslocation interfaces are provided directly to each wireless locationservice provider utilized).

Routing Applications

In one noteworthy routing application, hotels and other personal serviceproviders, such as auto rental agencies, resorts and cruise ships mayprovide inexpensive or free wireless concierge services to theircustomers, wherein an inexpensive MS 140 can offered to customers thatcan be used substantially only for contacting: (i) the personal service,(ii) emergency services, (iii) receiving directions to return to thepersonal service, and/or (iv) routing or directing customerspredetermined locations such as historic sites, shopping areas, and/orentertainment. In a similar fashion, instead of providing such adedicated MS 140, the personal service could in an alternativeembodiment, allow customers access such information from their ownpersonal mobile stations 140. In one embodiment, this may beaccomplished by allowing a user to attach such information to their userprofiles and thereby obtain at least temporary access to a wirelessconcierge providing one or more of the location based services (i)-(iv)immediately above. Accordingly, the MS 140 may be wirelessly locatedduring operations (ii) and (iii) via wireless communications between theMS 140 and a local commercial wireless service provider wherein arequest to locate the MS 140 is provided to, e.g., the gateway 142, andthe resulting MS location estimate is: (1) provided to a public safetyemergency center (e.g., E911) for dispatching emergency services, or (2)provided to a mapping and routing system such as provided by MapInfo ordisclosed in the LeBlanc et. al. patent application filed Jan. 22, 1999and having U.S. Pat. No. 6,236,365 (which is fully incorporated hereinby reference) so that the MS 140 user may be routed safely andexpeditiously to a predetermined location of the personal service. Notethat data representing the location of the personal service can beassociated with an identification of the MS 140 so that MS activationfor (iii) above results in one or more audio and/or visual presentationsof directions for directing the user to return to the personal service.

Additionally, directions to such personal services may be made availableto the personal MS 140 of a user, wherein upon calling a number (oraccessing a website via the MS), the directions to a desired destinationmay be transmitted to the MS and presented to the user. Moreover, suchdirections may be dependent upon how the MS user is traveling. Forexample, if it is known (or presumed) that the user is in a vehicle suchas an auto, the user may be directed first to a parking garage ratherthan to the front door of a government agency building. Alternatively,if it is known (or presumed) that the user is on foot, then the MS usermay indeed be directed to the front door of the government agencybuilding. Similarly, if the MS 140 is determined to be on a train,bicycle, watercraft, etc. such modes of conveyance may be used indetermining an appropriate route to present to the MS user. In oneembodiment of the present disclosure, traffic congestion may also beused to determine an appropriate route to present to the MS user.

Moreover, it is an aspect of the present disclosure that the MS 140 usermay be tracked by, e.g., periodic MS location determinations, until theMS user is substantially at the personal service. Note that if the MS140 user does not correctly follow the directions received, then for apredetermined deviation (e.g., dependent upon whether it is perceivedthat the user is on foot or in a vehicle, which may be determinedaccording to the user's velocity and/or acceleration) the MS user may bealerted to the deviation and a new route determined dependent upon,e.g., the user's new location, the direction that the user is traveling,and/or the mode of transportation. For example, if the MS 140 user goton an subway train, then after one or more locations of the MS user havebeen performed, if such locations are sufficiently accurate, it can bedetermined whether the user is proceeding along a route consistent withdirections provided, and that the user is on the subway. In the casewhere the MS user got onto the wrong subway train, the user can bealerted of this fact and given the opportunity to have a new routedetermined which takes into account not only the user's location, butwhere the user can exit the subway train, and likely, the subway trainschedules for expeditiously getting the MS user to his/her destination.

The MS 140 and the MS location providing wireless network (e.g., a CMRS,a PSTN 124 or the Internet 468) may also provide the MS user with theability to explicitly request to be substantially continuously tracked,wherein the MS tracked locations are stored for access by those havingpermission (e.g., the user, parents and/or associates of the user).Additionally, the velocity and/or expected time of arrival at apredetermined destination may be derived from such tracking and may beprovided to the user or his/her associates (e.g., employer, friends,and/or family). Further, note that this tracking and notification ofinformation obtained therefrom may be provided via a commercialtelephony or Internet enabled mobile station, or a mobile station inoperable communication with a short messaging service. For example, theMS registered owner may provide permissions for those able to accesssuch MS tracking information so that such information can beautomatically provided to certain associates and/or provided on requestto certain associates. Additionally, note that the MS 140 and the MSlocation providing wireless network may also allow the MS user todeactivate such MS tracking functionality. In one embodiment, an MS usermay activate such tracking for his/her MS 140 during working hours anddeactivate such tracking during non-working hours. Accordingly, anemployer can then track employee's whereabouts during work hours, whilethe employee is able to retain his/her location privacy when not workingalthough the employer may be still able to contact the employee in caseof an emergency during the employee's non-working time. Note, that thislocation capability and method of obtaining location information aboutan MS user while assuring privacy at other times may be useful forappropriately monitoring in personnel in the military, hospitals,transportation services (e.g., for couriers, bus and taxis drivers),telecommunications personnel, emergency rescue and correctionalinstitution personnel. Further, note that this selective MS locationcapability may be performed in a number of ways. For example, the MS 140may activate and deactivate such tracking by dialing a predeterminednumber (e.g., by manually or speed dialing the number) for switchingbetween activation of a process that periodically requests a wirelesslocation of the MS 140 from, e.g., the location gateway 142. Note thatthe resulting MS location information may be made available to otherusers at a predetermined phone number, Internet address or havingsufficient validation information (e.g., a password). Alternatively, theMS location providing wireless network may automatically activate suchMS tracking for predetermined times of the day and for predetermineddays of the week. Note that this latter embodiment may be particularlyuseful for both tracking employees, e.g., at large construction sites,and, e.g., determining when each employee is at his/her work site. Thus,in this embodiment, the MS location providing wireless network mayprovide database storage of times and days of the week for activationand deactivation of this selective MS tracking capability that isaccessible via, e.g., a network service control point 104 (or othertelephony network control points as one skilled in the art willunderstand), wherein triggers may be provided within the database forgenerating a network message (to, e.g., the gateway 142) requesting thecommencement of tracking the MS 140 or the deactivation of suchtracking. Accordingly, the resulting MS location information may beprovided to an employer's tracking and payroll system so that theemployer is able to determine the actual time an employee arrives at andleaves a work location site.

In another routing related application of the present disclosure, an MS140 and the MS location providing wireless network may provide the MSuser with functionality to register certain locations so that datarepresenting such locations can be easily accessed for use at a latertime. For example, the MS 140 user may be staying at a hotel in anunfamiliar area. Accordingly, using the present capability of thedisclosure, the user can request, via his/her MS 140, that his/herlocation at the hotel be determined and registered so that it isavailable at a later time for routing the user back to the hotel. Infact, the user may have personal location registrations of a pluralityof locations in various cities and countries so that when traveling theuser has wireless access to directions to preferred locations such ashis/her hotel, preferred restaurants, shopping areas, scenic areas,rendezvous points, theatres, athletic events, churches, entertainmentestablishments, locations of acquaintances, etc. Note, that suchpersonal location registration information may reside primarily on theuser's subscriber network, but upon the MS user's request, his/herpersonal location registrations may be transmitted to another networkfrom which the user is receiving wireless services as a roamer.Moreover, any new location registrations (or deletions) may beduplicated in the user's personal registration of the user's subscribernetwork. However, in some instances an MS user may wish to retain suchregistered locations only temporarily while the user is in a particulararea; e.g., a predetermined network coverage area. Accordingly, the MSuser may indicate (or such may be the default) that a new personallocation registration be retained for a particular length of time,and/or until a location of the user is outside the area to which suchnew location registrations appear to be applicable. However, prior todeleting any such registrations, the MS user may be queried to confirmsuch deletions. For example, if the MS user has new locationregistrations for the Dallas, Tex. area, and the MS user subsequentlytravels to London, then upon the first wireless location performed bythe MS user for location registration services, the MS user may bequeried as to whether to save the new Dallas, Tex. locationregistrations permanently, for an particular length of time (e.g. 30days), or delete all or selected portions thereof.

Other routing related applications of the present disclosure are forsecurity (e.g., tracking how do I get back to my hotel safely), and,e.g., sight seeing guided tour where the is interactive depending onfeedback from users

Roaming Services

Roaming services such as wireless concierge services that may offered totravelers by, e.g., hotels, resorts, theme parks, and/or ski areas.Additionally and/or alternatively, a user 2008 may be able to store andassociate a location with a user input description (and possibly apicture if the user's MS 140 supports such) and store such informationso that it is available at a later time, e.g., when the user is onceagain in the same geographical area.

There may also be roaming services wherein the various portions of theuser's profile and/or attachments thereto may become active depending onthe geographical location of the user. For example, a hotel chain mayoffer regional and/or global wireless concierge services wherein locallocation based information, such as pre-selected restaurants, shoppingareas, points of interest, entertainment, exercise areas, travel routes,bus (train or boat) schedules, parking areas (e.g., that may besubsidized by the hotel chain), sports equipment rentals, emergencyservices (police, fire, etc.), that is in a geographical area (such as ametropolitan area, a resort area, a theme park or other relatively localarea) where the user is located is automatically activated as the“current” set of locations to receive priority when the user enters arequest that can be satisfied by entities identified in such locallocation based information. Note that a potentially simple embodiment ofthis aspect of the present disclosure may be for the hotel chain to havean Internet website having for each of their hotels, corresponding webpages dedicated to local location based information in geographic areassurrounding the hotel. Such web pages may provide searching and routingcapabilities related to the local location base information forrelatively local geographical areas surrounding the hotel and these webpages may be made the default wireless concierge service capability. Inone embodiment, a user's profile (or specific portions thereof)maintained, e.g., (i) by a network service, such as a wireless carrier,(ii) by the user himself (i.e., on the user's MS 140, assuming theuser's MS 140 has sufficient storage capacity), (iii) by an electronicyellow pages entity, (iv) by an Internet search engine, may be madeavailable (at least temporarily) to the hotel's Internet wirelessconcierge capabilities so that user service requests can be easilycustomized to the user's preferences. Moreover, such Internet access mayprovide access (at least while the user is staying at the hotel) todiscounts, coupons, and/or free access to various local facilities.

Advertising Applications

Advertising may be directed to an MS 140 according to its location. Inat least some studies it is believed that MS 140 users do not respondwell to unsolicited wireless advertisement whether location based orotherwise. However, in response to certain user queries for locallyavailable merchandise, certain advertisements may be viewed as morefriendly. Thus, by allowing an MS user to contact, e.g., a wirelessadvertising portal by voice or via wireless Internet, and describecertain products or services desired (e.g., via interacting with anautomated speech interaction unit), the user may be able to describe andreceive (at his/her MS 140) audio and/or visual presentations of suchproducts or services that may satisfy such a user's request. Forexample, a user may enter a request: “I need a Hawaiian shirt, who hassuch shirts near here?”

In the area of advertising, the present disclosure has advantages bothfor the MS user (as well as the wireline user), and for product andservice providers that are nearby to the MS user. For instance, an MSuser may be provided with (or request) a default set of advertisementsfor an area when the MS user enters the area, registers with a hotel inthe area, or makes a purchase in the area, and/or requests informationabout a particular product or service in the area. Moreover, there maybe different collections of advertisements for MS users that arebelieved to have different demographic profiles and/or purposes forbeing in the area. Accordingly, an MS whose location is being determinedperiodically may be monitored by an advertisement wizard such that thiswizard may maintain a collection the MS user's preferences, and needs sothat when the MS user comes near a business that can satisfy such apreference or need, then an advertisement relating to the fulfillment ofthe preference or need may be presented to the MS user. However, it isan aspect of the disclosure that such potential advertisingpresentations be intelligently selected using as much information aboutthe user as is available. In particular, in one embodiment of thedisclosure MS user preferences and needs may be ordered according toimportance. Moreover, such user preferences and needs may be categorizedby temporal importance (i.e., must be satisfied within a particular timeframe, e.g., immediately, today, or next month) and by situationalimportance wherein user preferences and needs in this category are lesstime critical (e.g., do not have to satisfied immediately, and/or withina specified time period), but if certain criteria are meet the user willconsider satisfying such a preference or need. Thus, finding a Chineserestaurant for dinner may be in the temporal importance category whilepurchasing a bicycle and a new pair of athletic shoes may be ordered aslisted here in the situational category. Accordingly, advertisements forChinese restaurants may be provided to the user at least partiallydependent upon the user's location. Thus, once such a restaurant isselected and routing directions are determined, then the advertisingwizard may examine advertisements (or other available productinventories and/or services that are within a predetermined distance ofthe route to the restaurant for determining whether there is product orservice along the route that could potentially satisfy one of the user'spreferences or needs from the situational importance category. If so,then the MS user be may provided with the option of examining suchproduct or service information and registering the locations of userselected businesses providing such products or services. Accordingly,the route to the restaurant may be modified to incorporate detours toone or more of these selected businesses. The flowchart of FIGS. 23A and23B provides steps that illustrate the modification (if necessary) ofsuch a route so that the MS user can visit one or more locations alongthe route for accessing one or more additional products or services.

Of course, an MS user's situationally categorized preferences and needsmay allow the MS user to receive unrequested advertising during othersituations as well. Thus, whenever an MS user is moving such anadvertisement wizard (e.g., if activated by the user) may attempt tosatisfy the MS user's preferences and needs by presenting to the useradvertisements of nearby merchants that appear to be directed to suchuser preferences and needs.

Accordingly, for MS user preferences and needs, the wizard will attemptto present information (e.g., advertisements, coupons, discounts,product price and quality comparisons) related to products and/orservices that may satisfy the user's corresponding preference or need:(a) within the time frame designated by the MS user when identified ashaving a temporal constraint, and/or (b) consistent with situationalcriteria provided by the MS user (e.g., item on sale, item is less thana specified amount, within a predetermined traveling distance and/ortime) when identified as having a situational constraint. Moreover, suchinformation may be dependent on the geolocation of both the user and amerchant(s) having such products and/or services. Additionally, suchinformation may be dependent on a proposed or expected user route (e.g.,a route to work, a trip route). Thus, items in the temporal category areordered according how urgent must a preference or need must besatisfied, while items in the situational category may be substantiallyunordered and/or ordered according to desirableness (e.g., an MS usermight want a motorcycle of a particular make and maximum price, want anew car more). However, since items in the situational category may befulfilled substantially serendipitous circumstances detected by thewizard, various orderings or no ordering may be used. Thus, e.g., if theMS user travels from one commercial area to another, the wizard maycompare a new collection of merchant products and/or services againstthe items on an MS user's temporal and situational lists, and at leastalerting the MS user that there may be new information available about auser desired service or product which is within a predeterminedtraveling time from where the user is. Note that such alerts may bevisual (e.g., textual, or iconic) displays, or audio presentationsusing, e.g., synthesized speech (such as “Discounted motorcycles aheadthree blocks at Cydes Cycles”).

Note that the advertising aspects of the present disclosure may beutilized by an intelligent electronic yellow pages which can utilize theMS user's location (and/or anticipated locations; e.g., due to roadwaysbeing traversed) together with user preferences and needs (as well asother constraints) to both intelligently respond to user requests aswell as intelligently anticipate user preferences and needs. A blockdiagram showing the high level components of an electronic yellow pagesaccording to this aspect of the present disclosure is shown in FIG. 19.Accordingly, in one aspect of the present disclosure advertising is userdriven in that the MS user is able to select advertising based onattributes such as: merchant proximity, traffic/parking conditions, theproduct/service desired, quality ratings, price, user merchantpreferences, product/service availability, coupons and/or discounts.That is, the MS user may be able to determine an ordering ofadvertisements presented based on, e.g., his/her selection inputs forcategorizing such attributes. For example, the MS user may requestadvertisements athletic shoes be ordered according to the followingvalues: (a) within 20 minutes travel time of the MS user's currentlocation, (b) midrange in price, (c) currently in stock, and (d) nopreferred merchants. Note that in providing advertisements according tothe MS user's criteria, the electronic yellow pages may have to makecertain assumptions such if the MS user does not specify a time forbeing at the merchant, the electronic yellow pages may default the timeto a range of times somewhat longer than the travel time thereby goingon the assumption that MS user will likely be traveling to an advertisedmerchant relatively soon. Accordingly, the electronic yellow pages mayalso check stored data on the merchant to assure that the MS user canaccess the merchant once the MS user arrives at the merchant's location(e.g., that the merchant is open for business). Accordingly, the MS usermay dynamically, and in real time, vary such advertising selectionparameters for thereby substantially immediately changing theadvertising being provided to the user's MS. For example, the MS displaymay provide an area for entering an identification of a product/servicename wherein the network determines a list of related or complementaryproducts/services. Accordingly, if an MS user desires to purchase awedding gift, and knows that the couple to be wed are planning a trip toAustralia, then upon the MS user providing input in response toactivating a “related products/services” feature, and then inputting,e.g., “trip to Australia” (as well as any other voluntary informationindicating that the purchase is for: a gift, for a wedding, and/or aprice of less than $100.00), then the intelligent yellow pages may beable to respond with advertisements for related products/services suchas portable electric power converter for personal appliances that isavailable from a merchant local (and/or non-local) to the MS user.Moreover, such related products/services (and/or “suggestion”)functionality may be interactive with the MS user. For example, theremay be a network response to the MS user's above gift inquiry such as“type of gift: conventional or unconventional?”. Moreover, the networkmay inquire as to the maximum travel time (or distance) the MS user iswilling to devote to finding a desired product/service, and/or themaximum travel time (or distance) the MS user is willing to devote tovisiting any one merchant. Note that in one embodiment of the electronicyellow pages, priorities may be provided by the MS user as to apresentation ordering of advertisements, wherein such ordering may beby: price

Note that various aspects of such an electronic yellow pages describedherein are not constrained to using the MS user's location. In general,the MS user's location is but one attribute that can be intelligentlyused for providing users with targeted advertising, and importantly,advertising that is perceived as informative and/or addresses currentuser preferences and needs. Accordingly, such electronic yellow pageaspects of the present disclosure are not related to a change in the MSuser's location over time also apply to stationary communicationstations such home computers wherein, e.g., such electronic yellow pagesare accessed via the Internet. Additionally, the MS user may be able toadjust, e.g., via iconic selection switches (e.g., buttons or toggles)and icon range specifiers (e.g., slider bars) the relevancy and acorresponding range for various purchasing criteria. In particular, oncea parameter is indicated as relevant (e.g., via activating a toggleswitch), a slider bar may be used for indicating a relative or absolutevalue for the parameter. Thus, parameter values may be for:product/service quality ratings (e.g., display given to highestquality), price (low comparable price to high comparable price), traveltime (maximum estimated time to get to merchant), parking conditions.

Accordingly, such electronic yellow pages may include the followingfunctionality:

-   -   (a) dynamically change as the user travels from one commercial        area to another when the MS user's location periodically        determined such that local merchant's are given preference;    -   (b) routing instructions are provided to the MS user when a        merchant is selected;    -   (c) provide dynamically generated advertising that is related to        an MS user's preferences or needs. For example, if an MS user        wishes to purchase a new dining room set, then such an        electronic yellow pages may dynamically generate advertisements        with dining room sets therein for merchants that sell them. Note        that this aspect of the present disclosure is can be        accomplished by having, e.g., a predetermined collection of        advertising templates that are assigned to particular areas of        an MS user's display wherein the advertising information        selected according to the item(s) that the MS user has expressed        a preferences or desire to purchase, and additionally, according        to the user's location, the user's preferred merchants, and/or        the item's price, quality, as well as coupons, and/or discounts        that may be provided. Thus, such displays may have a plurality        of small advertisements that may be selected for hyperlinking to        more detailed advertising information related to a product or        service the MS user desires. Note that this aspect of the        present disclosure may, in one embodiment, provide displays        (and/or corresponding audio information) that is similar to        Internet page displays. However, such advertising may        dynamically change with the MS user's location such that MS user        preferences and needs for a items (including services) having        higher priority are given advertisement preference on the MS        display when the MS user comes within a determined proximity of        the merchant offering the item. Moreover, the MS user may be        able dynamically reprioritize the advertising displayed and/or        change a proximity constraint so that different advertisements        are displayed. Furthermore, the MS user may be able to request        advertising information on a specified number of nearest        merchants that provide a particular category of products or        services. For example, an MS user may be able to request        advertising on the three nearest Chinese restaurants that have a        particular quality rating. Note, that such dynamically generated        advertising    -   (d) information about MS user's preferences and needs may be        supplied to yellow page merchants regarding MS user's reside        and/or travel nearby yellow subscriber merchant locations as        described hereinabove

The following is a high level description of some of the componentsshown in FIG. 19 of an illustrative embodiment of the electronic yellowpages of the present disclosure.

-   -   a. Electronic yellow pages center: Assists both the users and        the merchants in providing more useful advertising for enhancing        business transactions. The electronic yellow pages center may be        a regional center within the carrier, or (as shown) an        enterprise separate from the carrier. The center receives input        from users regarding preferences and needs which first received        by the user interface.    -   b. User interface: Receives input from a user that validates the        user via password, voice identification, or other biometric        capability for identifying the user. Note that the that the        identification of user's communication device (e.g., phone        number) is also provided. For a user contact, the user interface        does one of: (a) validates the user thereby allowing user access        to further electronic yellow page services, (b) requests        additional validation information from the user, or (c)        invalidates the user and rejects access to electronic yellow        pages. Note that the user interface retrieves user        identification information from the user profile database        (described hereinbelow), and allows a validated user to add,        delete, and/or modify such user identification information.    -   c. User ad advisor: Provides user interface and interactions        with the user. Receives an identification/description of the        user's communication device for determining an appropriate user        communication technique. Note that the user ad advisor may also        query (any) user profile available (using the user's identity)        for determining a preferred user communication technique        supported by the user's communication device. For example, if        the user's communication device supports visual presentations,        then the user ad advisor defaults to visual presentations unless        there are additional constraints that preclude providing such        visual presentations. In particular, the user may request only        audio ad presentations, or merely graphical pages without video.        Additionally, if the user's communication supports speech        recognition, then the user ad advisor may interact with user        solely via verbal interactions. Note that such purely verbal        interactions may be preferable in some circumstances such as        when the user can not safely view a visual presentation; e.g.,        when driving. Further note that the user's communication device        may sense when it is electronically connected to a vehicle and        provide such sensor information to the user ad advisor so that        this module will then default to only a verbal presentation        unless the user requests otherwise. Accordingly, the user ad        advisor includes a speech recognition unit (not shown) as well        as a presentation manager (not shown) for outputting ads in a        form compatible both with the functional capabilities of the        user's communication device and with the user's interaction        preference.    -   Note that the user ad advisor communicates: (a) with the user ad        selection engine for selecting advertisements to be presented to        the user, (b) with the user profile database for inputting        thereto substantially persistent user personal information that        can be used by the user ad selection engine, and for retrieving        user preferences such as media preference(s) for presentations        of advertisements, and (c) with the user preference and needs        satisfaction agents for instantiating intelligent agents (e.g.,        database triggers, initiating merchant requests for a        product/service to satisfy a user preference or need).    -   Also note that in some embodiments of the present disclosure,        the user ad advisor may also interact with a user for obtaining        feedback regarding: (a) whether the advertisements presented,        the merchants represented, and/or the products/services offered        are deemed appropriate by the user, and (b) the satisfaction        with a merchant with which the user has interactions. In        particular, such feedback may be initiated and/or controlled        substantially by the user preference and needs satisfaction        agent management system (described hereinbelow).    -   d. User profile database: A database management system for        accessing and retaining user identification information, user        personal information, and identification of the user's        communication device (e.g., make, model, and/or software        version(s) being used). Note that the user profile database may        contain information about the user that is substantially        persistent; e.g., preferences for: language (e.g., English,        Spanish, etc.), ad presentation media (e.g., spoken, textual,        graphical, and/or video), maximum traveling time/distance for        user preferences and needs of temporal importance (e.g., what is        considered “near” to the user), user demographic information        (e.g., purchasing history, income, residential address, age,        sex, ethnicity, marital status, family statistics such as number        of child and their ages), and merchant preferences/preclusions        (e.g., user prefers one restaurant chain over another, or the        user wants no advertisements from a particular merchant).    -   e. User ad selection engine: This module selects advertisements        that are deemed appropriate to the user's preferences and needs.        In particular, this module determines the categories and        presentation order of advertisements to be presented to the        user. To perform this task, the user ad selection engine uses a        user's profile information (from the user profile database), a        current user request (via the user ad advisor), and/or the        user's current geolocation (via the interface to the location        gateway 142). Thus, for a user requesting the location of an        Italian restaurant within ½ mile of the user's current location,        in a medium price range, and accepting out of town checks, the        user ad selection engine identifies the ad criteria within the        user's request, and determines the advertising categories        (and/or values thereof) from which advertisements are desired.        In one embodiment,    -   Note that the user ad selection engine can suggest advertisement        categories and/or values thereto to the user if requested to do        so.

When an MS 140 appears to be traveling an extended distance through aplurality of areas (as determined, e.g., by recent MS locations along aninterstate that traverse a plurality of areas), then upon entering eachnew area having a new collection of location registrations (and possiblya new location registration wizard) may be provided. For example, a newdefault set of local location registrations may become available to theuser. Accordingly, the user may be notified that new temporary locationregistrations are available for the MS user to access if desired. Forexample, such notification may be a color change on a video displayindicating that new temporary registrations are available. Moreover, ifthe MS user has a personal profile that also is accessible by a locationregistration wizard, then the wizard may provide advertising for localbusinesses and services that are expected to better meet the MS user'stastes and needs. Thus, if such wizard knows that the MS user prefersfine Italian food but does not want to travel more than 20 minutes byauto from his/her hotel to reach a restaurant, then advertisements forrestaurants satisfying such criteria will become available to the userHowever, MS users may also remain anonymous to such wizards, wherein the

Note, that by retaining MS user preferences and needs, if permission isprovided, e.g., for anonymously capturing such user information, thisinformation could be provided to merchants. Thus, merchants can get anunderstanding of what nearby MS user's would like to purchase (and underwhat conditions, e.g., an electric fan for less than $10). Note suchuser's may be traveling through the area, or user's may live nearby.Accordingly, it is a feature of the present disclosure to providemerchant's with MS user preferences and needs according to whether theMS user is a passerby or lives nearby so that the merchant can bettertarget his/her advertising.

In one embodiment, a single wizard may be used over the coverage area ofa CMRS and the database of local businesses and services changes as theMS user travels from one location registration area to another.Moreover, such a wizard may determine the frequency and when requestsfor MS locations are provided to the gateway 142. For example, suchdatabases of local businesses and services may be coincident with LATAboundaries. Additionally, the wizard may take into account the directionand roadway the MS 140 is traveling so that, e.g., only businesseswithin a predetermined area and preferably in the direction of travel ofthe MS 140 are candidates to have advertising displayed to the MS user.

The flowchart of FIGS. 24A and 24B is illustrative of the stepsperformed when, e.g., MS user input of preferences and needs isiteratively examined at various user locations for determining thelocation(s) that sufficiently satisfy user specified constraints (e.g.,temporal or situational constraints) so that the user is alerted ornotified of products and/or services that satisfy the user's input. Thesteps 2004 through 2040 are fully disclosed and explained in thesections hereinabove.

Points of Interest Applications

Embodiments of the present disclosure can used for sight seeing guidedtours where such embodiments are interactive depending on feedback fromusers. Such interactivity being both verbal descriptions and directionsto points of interest.

Security Applications

Embodiments of the present disclosure may provide Internet picturecapture with real time voice capture and location information forsightseeing, and/or security.

The foregoing description of preferred embodiments of the presentdisclosure has been provided for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed herein. Modifications andvariations commensurate with the description herein will be apparentthose skilled in the art and are intended to be within the scope of thepresent disclosure to the extent permitted by the relevant art. Theembodiments provided are for enabling others skilled in the art tounderstand the disclosure, its various embodiments and modifications asare suited for uses contemplated. It is intended that the scope of theinvention be defined by the following claims and their equivalents.

1. A method for providing network services to a plurality of mobileunits, wherein each of the mobile units is located using wireless signalmeasurements obtained from transmissions between said mobile unit and aplurality of fixed location communication stations, wherein each of saidcommunications stations includes one or more of a transmitter and areceiver for wirelessly communicating with said mobile unit, comprising:providing first and second mobile unit location evaluators, wherein eachof said location evaluators determine information related to thelocation of said mobile station units when said location estimator issupplied with data appropriate for responding with information relatedto a location of one of the mobile units, wherein: (A) said firstlocation evaluator determines information related to the location for amobile unit (M), by performing one or more of the following techniques(i) through (x) when the first location evaluator is supplied with acorresponding instance of said appropriate data, wherein the appropriatedata includes values obtained from wireless signal measurements providedby transmissions between the mobile unit M and the communicationstations: (i) a first technique for determining, for at least one of thecommunication stations, one of: a distance, and a time difference ofarrival between the mobile unit M and the at least one communicationstation, wherein said first technique estimates a time of arrival (TOA)of a received signal relative to a time reference at each one of aplurality of wireless signal monitoring stations using an inversetransform whose resolution is greater than Rayleigh resolution; (ii) asecond technique for estimating a location of said mobile unit M, usingvalues from a corresponding instance of said appropriate data obtainedfrom signals received by the mobile unit M from one or more satellites;(iii) a third technique for recognizing a pattern of characteristics ofa corresponding instance of said appropriate data, wherein said patternof characteristics is indicative of a plurality of wireless signaltransmission paths between the mobile unit M and each of one or more ofthe communication stations; and (iv) a fourth technique for estimating alocation of said mobile unit M using a USW model, wherein the followingsteps (iv-a)-(iv-d) are performed: (iv-a) receiving at an antenna arrayprovided at one of the communication stations, signals originating fromthe mobile unit M, wherein the signals comprise p-dimensional arrayvectors sampled from p antennas of the array; (iv-b) determining fromthe received signals, a signal signature, wherein the signal signaturecomprises a measured subspace, wherein the array vectors areapproximately confined to the measured subspace; (iv-c) comparing thesignal signature to a database comprising calibrated signal signaturesand corresponding location data, wherein the comparing comprisescalculating differences between the measured subspace and calibratedsubspaces; and (iv-d) selecting from the database a most likelycalibrated signal signature and a corresponding most likely location ofthe mobile unit M by using the calculated differences; (v) a fifthtechnique for estimating a location of said mobile unit M using an Emodel, wherein the following steps (v-a)-(v-e) are performed: (v-a)receiving, at a multiplicity of the communication stations, a signaltransmitted by the mobile unit M; (v-b) forwarding, by each of amultiplicity of the communication stations, said received signal andtiming information to a central processing center; (v-c) calculating,within said central processing center, a time difference of arrival(TDOA) location estimate of said mobile unit M based upon said timinginformation; (v-d) calculating, within said central processing center, atiming advance (TA) location estimate of said mobile unit M based uponsaid timing information; and (v-e) determining said position of saidmobile unit M using said TDOA and TA location estimates; (vi) a sixthtechnique for estimating a location of said mobile unit M using an STmodel, wherein the following steps (a)-(e) are performed: (vi-a)receiving, in a SPS receiver co-located with the mobile unit M, SPSsignals from at least one SPS satellite; (vi-b) transmitting cell basedcommunication signals between: a communications system having a first ofthe communication stations coupled to said SPS receiver, and a second ofthe communication stations which is remotely positioned relative to saidmobile unit M, wherein said cell based communication signals arewireless; (vi-c) determining a first time measurement which represents atime of travel of a message in said cell based communication signals ina cell based communication system having at least some of thecommunication stations which comprises said second communication stationand said communication system; (vi-d) determining a second timemeasurement which represents a time of travel of said SPS signals;(vi-e) determining a position of said mobile unit M from at least saidfirst time measurement and said second time measurement, wherein saidcell based communication signals are capable of communicating datamessages in a two-way direction between said first cell basedtransceiver and said communication system; (vii) a seventh technique forestimating a location of said mobile unit M using an TE model, whereinthe following steps (vii-a)-(vii-l) are performed: (vii-a) transmittingfrom said mobile unit M, m samples of a signal; (vii-b) receiving at oneof the communication stations, said m samples together with multipathcomponents and noise; (vii-c) determining an estimated channel powerprofile for each of said m samples; (vii-d) selecting a first set of Nsamples from said m samples; (vii-e) performing incoherent integrationfor said estimated channel power profiles for said first set of Nsamples to form a first integrated signal; (vii-f) if a quality level ofsaid first integrated signal with respect to signal to noise is lessthan a predetermined threshold, selecting another sample from said Msamples; (vii-g) performing incoherent integration for said estimatedchannel power profiles for said first set of N samples and said anothersample to form a second integrated signal; (vii-h) if a quality level ofsaid second integrated signal with respect to signal to noise is greaterthan or equal to said predetermined threshold, determining atime-of-arrival of a maximum level of said second integrated signal;(vii-i) entering said time-of-arrival into a time-of-arrival versusfrequency of occurrence array; (vii-j) selecting a second set of Nsamples from said m samples; (vii-k) repeating all of said performingthrough said entering steps for said second set of N samples; and(vii-l) determining a minimum value estimated time-of-arrival from saidarray; (viii) an eighth technique for estimating a location of saidmobile unit M using an SigT model, wherein the following steps(viii-a)-(viii-e) are performed: (viii-a) within the mobile unit M,transmitting a locating signal composed of at least two tone components;(viii-b) within each of a plurality of the communication stations,receiving the locating signal at one or more antennas, and within atleast one of the communication stations, receiving the locating signalwith at least two antennas; (viii-c) coupling each antenna to areceiver; (viii-d) within each receiver, generating amplitude and phasevalues from the locating signal as received by the antenna, the valuesindicative of amplitude and phase of at least two tone components of thelocating signal, as received at the corresponding antenna and measuredat defined times; and (viii-e) combining the values indicative ofamplitude and phase for the tone components from a plurality of thereceivers to determine the position of the mobile unit M; (ix) an ninthtechnique for estimating a location of said mobile unit M using a TLMEmodel, wherein the following steps (ix-a)-(ix-h) are performed thereforin a mobile radio system providing at least some of the communicationstations, said mobile radio system including a network controller and atleast three of the communication stations, each of said at least threecommunication stations including an uplink TOA measuring unit operableto communicate with said network controller, a control unit, and a timereference unit operable to provide timing reference signals to saiduplink TOA measuring unit, at least one of said at least threecommunication stations co-located with and connected to a second mobileunit M₂, said second mobile unit M₂ coupled to said network controllervia a radio interface, and a service node operable to store knownpositions of at least two of said at least three communication stations:(ix-a) receiving a request in said mobile radio system to determine thegeographical position of said mobile unit M; (ix-b) determining andreporting the position of said second mobile unit M₂ to said servicenode; (ix-c) directing said mobile unit M to transmit digital signalsuplink on a traffic channel when said mobile unit M is not transmittingor transmitting only analog signals; (ix-d) measuring in each uplink TOAmeasuring unit an uplink TOA of the digital signals transmitted by themobile unit M; (ix-e) receiving in said network controller said uplinkTOA measurements from said at least three communication stations and atraffic channel number to said traffic channel; (ix-f) translating saidtraffic channel number to an identity of said mobile unit M; (ix-g)conveying said uplink TOA measurements and said mobile unit M identityto said service node; and (ix-h) calculating in said service node theposition of said mobile unit M using said known positions of said atleast three communication stations and said uplink TOA measurements; (x)a tenth technique for estimating a location of said mobile unit M usingan N model, wherein the following steps (x-a)-(x-d) are performed: (x-a)receiving global positioning system satellite (GPS) signals from aplurality of global positioning system satellites; (x-b) receiving aplurality of cellular position signals that do not contain data in aGPS-like format; (x-c) calculating the geographic position of the mobileunit M using said received global positioning system satellite signalswhen a requisite number of the plurality of global positioning systemsatellites are in view of a global positioning system receiver; and(x-d) calculating the geographic position of the mobile unit M usingboth said received plurality of cellular position signals andsubstantially all of said received global positioning system satellitesignals when the requisite number of the plurality of global positioningsystem satellites are not in view of the global positioning systemreceiver; (B) for at least a particular one of said techniques (A)(i)through (A)(x) performed by said first location evaluator, said secondlocation evaluator performs a different one of said techniques whensupplied with a corresponding instance of said appropriate data for thedifferent technique; receiving, from a plurality of requesting sources,requests for location based services; first obtaining, from said firstlocation evaluator, first location related information for one of themobile units in response to a first of the requests; second obtaining,from said second location evaluator, second location related informationfor one of the mobile units in response to a second of the requests;first determining a first destination for a first of the location basedservices for transmitting a first estimated location obtained from thefirst location related information; second determining a seconddestination for a second of the location based services for transmittinga second estimated location obtained from the second location relatedinformation; first communicating, to a first receiving source, a firstcommunication responsive to the first request, wherein said firstcommunication is dependent upon a first result from the first locationbased service; wherein the first location based service determines thefirst result after receiving the first estimated location; wherein thefirst request is used in identifying the first receiving source; secondcommunicating, to a second receiving source, a second communicationresponsive to the second request, wherein said second communication isdependent upon a second result from the second location based service;wherein the second location based service determines the second resultafter receiving the second estimated location; wherein the secondrequest is used in identifying the second receiving source; wherein thesteps of at least one of (1) and (2) following are performed: (1) firstcontacting a billing system for authorizing access to network servicesfor the first request; wherein the first contacting step is performedprior to the first estimated location being determined; secondcontacting the same billing system for authorizing access to networkservices for the second request;  wherein the second contacting step isperformed prior to the second estimated location being determined; and(2) the step of first determining, includes a step of selecting thefirst location based service from a plurality of location based serviceswherein each of the plurality of location based services is forfulfilling the first request, wherein said step of selecting includesobtaining data indicative of a load on each of two of the plurality oflocation based services and using the data to determine the firstdestination.
 2. A method as claimed in claim 1, wherein said selectingstep, of said first determining step, is performed.
 3. A method asclaimed in claim 1, wherein said step of first contacting is performed.4. A method for providing network services to a plurality of mobileunits, wherein each of the mobile units is located using wireless signalmeasurements obtained from transmissions between said mobile unit and aplurality of fixed location communication stations, wherein each of saidcommunications stations includes one or more of a transmitter and areceiver for wirelessly communicating with said mobile unit, and whereinthere are first and second mobile unit location evaluators, wherein eachof said location evaluators determine information related to thelocation of said mobile units when said location estimator is suppliedwith data appropriate for responding with information related to alocation of one of the mobile units, wherein: (A) at least said firstlocation evaluator determines information related to the location for amobile unit (M), by performing one or more of the following techniques(i), (ii) and (iii) when the first location evaluator is supplied with acorresponding instance of said appropriate data, wherein the appropriatedata includes values obtained from wireless signal measurements providedby transmissions between the mobile unit M and the communicationstations: (i) a first technique for determining, for at least one of thecommunication stations, one of: a distance, and a time difference ofarrival between the mobile unit M and the at least one communicationstation, wherein said first technique estimates a time of arrival (TOA)of a received signal relative to a time reference at each one of aplurality of wireless signal monitoring stations using an inversetransform whose resolution is greater than Rayleigh resolution; (ii) asecond technique for estimating a location of said mobile unit M, usingvalues from a corresponding instance of said appropriate data obtainedfrom signals received by the mobile unit M from one or more satellites;(iii) a third technique for recognizing a pattern of characteristics ofa corresponding instance of said appropriate data, wherein said patternof characteristics is indicative of a plurality of wireless signaltransmission paths between the mobile unit M and each of one or more ofthe communication stations; comprising: receiving, from a plurality ofrequesting sources, requests for location based services; firstobtaining, from said first location evaluator, first location relatedinformation for a first of the mobile units in response to a first ofthe requests; second obtaining, from one of the said locationevaluators, second location related information for a second of themobile units in response to a second of the requests; first determininga first destination for a first of the location based services fortransmitting a first estimated location obtained from first locationrelated information; second determining a second destination for asecond of the location based services for transmitting a secondestimated location obtained from second location related information;first communicating, to a first receiving source, a first communicationresponsive to the first request, wherein said first communication isdependent upon a first result from the first location based service;wherein the first location based service determines the first resultafter receiving the first estimated location; wherein the first requestis used in identifying the first receiving source; second communicating,to a second receiving source, a second communication responsive to thesecond request, wherein said second communication is dependent upon asecond result from the second location based service; wherein the secondlocation based service determines the second result after receiving thesecond estimated location; wherein the second request is used inidentifying the second receiving source; wherein the steps of at leastone of (1) and (2) following are performed: (1) first contacting abilling system for authorizing access to network services for the firstrequest; wherein the first contacting step is performed prior to thefirst estimated location being determined; second contacting the samebilling system for authorizing access to network services for the secondrequest; wherein the second contacting step is performed prior to thesecond estimated location being determined; and (2) the step of firstdetermining, includes a step of selecting the first location basedservice from a plurality of location based services wherein each of theplurality of location based services is for fulfilling the firstrequest, wherein said step of selecting includes obtaining dataindicative of a load on each of two of the plurality of location basedservices and using the data to determine the first destination.
 5. Themethod as claimed in claim 4, wherein said selecting step, of said firstdetermining step, is performed.
 6. The method as claimed in claim 4,wherein said first mobile unit is co-located with a processor foractivating at least one of said location evaluators.
 7. A method forproviding network services to a plurality of mobile units, comprising:receiving, from a plurality of requesting sources, requests for locationbased services; wherein to fulfill a first of the requests, there is arequirement for at least a first estimated location of a first of aplurality of mobile units, said at least first estimated location foruse by a first location based service in fulfilling the first request;wherein to fulfill a second of the requests, there is a requirement forat least a second estimated location of a second of the mobile units,said at least second estimated location for use by a second locationbased service in fulfilling the second request; first determining afirst destination for the first location based service for transmittingthe first estimated location; second determining a second destinationfor the second location based service for transmitting the secondestimated location, wherein the first and second location based servicesare different; first communicating, to a first receiving source, a firstcommunication responsive to the first request, wherein said firstcommunication is dependent upon a first result from the first locationbased service; wherein the first location based service determines thefirst result after receiving the first estimated location; wherein thefirst request is used in identifying the first receiving source; secondcommunicating, to a second receiving source, a second communicationresponsive to the second request, wherein said second communication isdependent upon a second result from the second location based service;wherein the second location based service determines the second resultafter receiving the second estimated location; wherein the secondrequest is used in identifying the second receiving source; wherein thesteps of at least one of (a) and (b) following are performed: (a) firstcontacting a billing system for authorizing access to network servicesfor the first request; wherein the first contacting step is performedprior to the first estimated location being determined; secondcontacting the same billing system for authorizing access to networkservices for the second request; wherein the second contacting step isperformed prior to the second estimated location being determined; and(b) the step of first determining, includes a step of selecting thefirst location based service from a plurality of location based serviceswherein each of the plurality of location based services is forfulfilling the first request, wherein said step of selecting includesobtaining data indicative of a load on each of two of the plurality oflocation based services and using the data to determine the firstdestination.
 8. The method of claim 7, wherein at least the steps offirst and second contacting are performed.
 9. The method of claim 8,further including steps of requesting a wireless location of each thefirst and second mobile units.
 10. The method of claim 7, wherein thestep of selecting is performed.
 11. The method of claim 7, furtherincluding the steps of: requesting the first estimated location from afirst location service provider; requesting activation of the firstlocation based service; wherein the step of requesting the firstestimated location is performed prior to the step of requestingactivation of the first location service.